М.: O’Reilly Media, 2017. — 392 с. Машинное обучение стало неотъемлемой частью различных коммерческих и исследовательских проектов, однако эта область не является прерогативой больших компаний с мощными аналитическими командами. Даже если вы еще новичок в использовании Python, эта книга познакомит вас с практическими способами построения систем машинного обучения. При всем...
М.: МЦНМО, 2013. — 390 с.
Книга предназначена для первоначального знакомства с математическими основами современной теории машинного обучения (Machine Learning) и теории игр с предсказаниями. В первой части излагаются основы статистической теории машинного обучения, рассматриваются задачи классификации и регрессии с опорными векторами, теория обобщения и алгоритмы построения...
Massachusetts Institute of Technology, 2012. — 1067 p. — ISBN: 0262018020, 978-0262018029. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive...
СПб.: Питер, 2017. — 336 с. — (Библиотека программиста). — ISBN: 978-5-496-02989-6. Данная книга рассчитана на тех, кто хочет решать самые разнообразные задачи при помощи машинного обучения. Как правило, для этого нужен Python, поэтому в примерах кода используется этот язык, а также библиотеки pandas и scikit-learn. Вы познакомитесь с основными понятиями ML, такими как сбор...
М.: O’Reilly Media, 2017. — 392 с. Машинное обучение стало неотъемлемой частью различных коммерческих и исследовательских проектов, однако эта область не является прерогативой больших компаний с мощными аналитическими командами. Даже если вы еще новичок в использовании Python, эта книга познакомит вас с практическими способами построения систем машинного обучения. При всем...
СПб.: Диалектика, 2019. — 683 с. — ISBN: 9785950029622. Эта книга предполагает, что вы почти ничего не знаете о машинном обучении. Её цель — определить концепцию, методы и инструменты, необходимые для реализации программ, которые помогут изучать массивы данных. Используя конкретные практические примеры, с минимумом теории, а также две библиотеки для Python — Scikit-learn и...
Учебное пособие. Теория + Практикум (листинги) / В.В. Воронина, А. В. Михеев, Н. Г. Ярушкина, К. В. Святов. — Ульяновск : УлГТУ, 2017. — 290 с. Учебное пособие рассматривает вопросы, связанные с анализом данных: модели, алгоритмы, методы и их реализацию на языке Python. Особое внимание уделено анализу временных рядов. Книга предназначена для студентов группы направлений 09, а...
New York: Jason Brownlee., 2018. — 212 p. Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the...
2nd Edition. — O’Reilly, 2019. — 856 р. - ISBN: 1492032646 Final Edition Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book...
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to...
Machine Learning Mastery, 2016. — 179 p. The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this mega Ebook written in the friendly Machine Learning Mastery style...
Cambridge: Cambridge University Press, 2020. — 398 p. — ISBN: 110845514X. Machine learning is the latest in a long line of attempts to distill human knowledge and reasoning into a form that is suitable for constructing machines and engineering automated systems. As machine learning becomes more ubiquitous and its software packages become easier to use, it is natural and...
Wiley, 2015. — 360 p. — ISBN: 1118961749, 9781118961742 Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this...
М.: Lector.ru, 2019. — 93 с. В последнее время машинное обучение является мейнстримом современных компьютерных технологий. Можно уверенно сказать, что кроме умения программировать именно знание основ и практического применения методов машинного обучения становится стандартом для IT специалиста. Наше пособие посвящено методам машинного обучения. Мы рассматриваем большое...
O’Reilly, 2017. — 392 p. — ISBN: 9781449369415 Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data...
McGraw-Hill, 1997. 414 p. — ISBN 0070428077. This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning. The field of machine learning is concerned with the question...
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
Packt Publishing, 2017. — 450 p. — ISBN: 978-1-78829-575-8. True PDF Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain...
Machine Learning Mastery, 2016. — 223 p. — ISBN: N\A Do you want to do machine learning using R, but you’re having trouble getting started? In this post you will complete your first machine learning project using R. In this step-by-step tutorial you will: Download and install R and get the most useful package for machine learning in R. Load a dataset and understand it’s...
Hoboken: Wiley, 2018. — 393 p. Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big...
Packt Publishing, 2016. — 653 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689 This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working examples using a wide...
Packt Publishing, 2022. — 741 p. — ISBN 9781801819312. PyTorch book of the bestselling and widely acclaimed Python Machine Learning series expanded to include transformers, XGBoost, and graph neural networks Key Features: Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine...
Б.м.и.: Andriy Burkov, 2019. — 152 p. Is this book for you? You will enjoy the book if you are: - a software engineer or a scientist who wants to become a machine learning engineer or a data scientist - a data scientist trying to stay on the edge of the state-of-the-art and deepen their ML expertise - a manager who wants to feel confident while talking about AI with engineers...
O’Reilly Media, 2022. — 386 p. — ISBN-13: 978-1-098-10796-3. Machine learning systems are both complex and unique. They are complex because they consist of many different components and involve many different stakeholders. They are unique because they are data-dependent, and data varies wildly from one use case to the next. This book takes a holistic approach to designing...
3rd Edition. — O’Reilly Media, Inc., 2022. — 850 p. — ISBN 978-1-098-12597-4. Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete...
СПб.: Питер, 2019. — 306 с. — (Для профессионалов). — ISBN: 978-5-4461-1061-2. Искусственный интеллект - это мощный инструмент в руках современного архитектора, разработчика и аналитика. Облачные технологии - ваш путь к укрощению искусственного интеллекта. Тщательно изучив эту незаменимую книгу от Ноя Гифта, легендарного эксперта по языку Python, вы легко научитесь писать...
3rd ed. — MIT Press, 2014. — 640 p. — ISBN: 0262028182, 9780262028189 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be...
Springer, 2020. — 573 p. — ISBN: 3030410676. This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of...
Andriy Burkov, 2019. - 160 p. - ISBN: 199957950X. Final version ! Completed on 5.7.2019 Who This Book is For This book contains only those parts of the vast body of material on machine learning developed since the 1960s that have proven to have a significant practical value . A beginner in machine learning will find in this book just enough details to get a comfortable level of...
Пер. с англ. А. А. Слинкина. — М.: ДМК Пресс, 2015. — 400 c.: ил. — ISBN: 978-5-97060-273-7, 600 dpi, OCR. Перед вами один из самых интересных учебников по машинному обучению - разделу искусственного интеллекта, изучающего методы построения моделей, способных обучаться, и алгоритмов для их построения и обучения. Автор воздал должное невероятному богатству предмета и не упустил...
N.-Y.: Chapman and Hall/CRC, 2014. - 457p.
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer...
2016. — 162 p. The book was designed to teach developers about machine learning algorithms. It includes both procedural descriptions of machine learning algorithms and step-by-step tutorials that show exactly how to plug-in numbers into the various equations and exactly what numbers to expect on the other side.
3rd ed. — Morgan Kaufmann, 2011. — 630 p. — ISBN 0123748569 Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the...
Санкт-Петербург: БХВ-Петербург, 2019. — 300 с. — ISBN: 9785977540568. Книга содержит около 200 рецептов решения практических задач машинного обучения, таких как загрузка и обработка текстовых или числовых данных, отбор модели, уменьшение размерности и многие другие. Рассмотрена работа с языком Python и его библиотеками, в том числе pandas и scikit-leam. Решения всех задач...
Manning Publications, 2012. — 384 p. — ISBN: 1617290181, 9781617290183. Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting,...
Apress, 2017. - 358p. - ISBN: 978-1484228654 Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six...
Пер. с англ. Р. Чикин. — СПб: Питер, 2024. — 512 с.: ил. — (Библиотека программиста). — ISBN:978-5-4461-1923-3. Машинное обучение — это набор методов анализа данных, основанных на алгоритмах, которые дают все более точные результаты по мере поступления новых данных. Машинное обучение лежит в основе систем рекомендаций, программ распознавания лиц, «умных» колонок и даже...
John Wiley & Sons, 2016. — 435 p. — (For Dummies). — ISBN: 1119245516, 9781119245513. Your no-nonsense guide to making sense of machine learning. Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on...
O’Reilly, 2019. — 362 p. — ISBN: 1492035645. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled...
М.: Альпина ПРО, 2021. — 335 с. — ISBN 978-5-907394-29-2 Мы живем во время революции, еще 50 лет назад казавшейся невероятной, — революции в области умных машин, которые теперь обучаются самостоятельно, вместо того чтобы просто выполнять запрограммированные команды. И возможности таких машин огромны: распознавание изображений, лиц и голосов, переводы на сотни языков,...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner....
2nd Edition. — Packt Publishing, 2015. — 454 p. — ISBN: 978-1-78439-390-8 Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has...
O’Reilly Media, Inc., 2018. — 366 p. — ISBN: 978-1-491-98938-8. Целевая аудитория: опытные разработчики. Пожалуй, ни для кого не новость, что Python в последнее время популярен именно как инструмент для разработки в области машинного обучения и Data Science. Это руководство рассматривает современные подходы языка к решению актуальных проблем в данной области, принятые стандарты...
New York: Apress, 2018. — 712 p. Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As...
Packt, 2018. — 503 p. The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental,...
Springer, 2020. — 506 p. — ISBN: 3030403432. This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science . Advanced undergraduate...
Springer, 2018. — 493 p. — ISBN: 978-3319735306. ext analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1...
Пер. с англ. С. Черников. — СПб.: Питер, 2024. — 512 с.: ил. — (Для профессионалов). — ISBN: 978-5-4461-1788-8. Фундаментальные математические дисциплины, необходимые для понимания машинного обучения, — это линейная алгебра, аналитическая геометрия, векторный анализ, оптимизация, теория вероятностей и статистика. Традиционно все эти темы размазаны по различным курсам, поэтому...
New York: Jason Brownlee., 2018. — 291 p. — ISBN: N\A. The information contained within this eBook is strictly for educational purposes. If you wish to apply ideas contained in this eBook, you are taking full responsibility for your actions. The author has made every e_ort to ensure the accuracy of the information within this book was correct at time of publication. The author...
2nd Edition. Wiley-Interscience, 2001. - 738 pages. The ease with which we recognize a face, understand spoken words, read handwritten characters, identify our car keys in our pocket by feel, and decide whether an apple is ripe by its smell belies the astoundingly complex processes that underlie these acts of pattern recognition. Pattern recognition — the act of taking in raw...
Manning Publications Co., 2021. — 513 p. — ISBN 978-1617295911. Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble...
Packt Publishing, 2016. — 922 p. — ISBN: 978-1-78712-321-2. Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning...
Пер. с англ. и ред. Ю.Н. Артеменко. — O’Reilly Media, lnс; СПб.: Альфа-книга, 2019. — 688 с.: ил. — ISBN: 978-5-9500296-2-2 (рус.), ISBN: 978-1-491-96229-9 (англ.). "Эта книга - замечательное введение в теорию и практику решения задач с помощью нейронных сетей. Она охватывает ключевые моменты, необходимые для построения эффективных приложений, а также обеспечивает достаточную...
Academic Press, 2015. — 1072 p. Machine Learning is a name that is gaining popularity as an umbrella formethods that have been studied and developed for many decades in different scientific communities and under different names, such as Statistical Learning, Statistical Signal Processing, Pattern Recognition, Adaptive Signal Processing, Image Processing and Analysis, System...
O’Reilly, 2020. — 408 p. — ISBN 9781098115784. 2020-10-15: First Release The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of...
Independently published, 2020. — 302 p. — ISBN B08NZW37N6. 4 Books in 1! Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading...
Scatterplot Press, 2017. — 155 p. — ISBN: 1549617214. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey...
O’Reilly Media, 2020. — 432 p. — ISBN: 978149207305-5. Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised,...
Выходные данные не известны. — 140 с. Введение: задачи обучения по прецедентам . Основные понятия и определения. Примеры прикладных задач. Байесовские методы классификации . Вероятностная постановка задачи классификации. Непараметрическая классификация. Нормальный дискриминантный анализ. Многомерное нормальное распределение. Разделение смеси распределений. Метрические методы...
2nd edition. — Elsevier, 2005. Features Explains how data mining algorithms work. Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques. Covers performance improvement techniques, including input preprocessing and combining output from different methods. Shows you how to use the Weka machine learning...
Packt Publishing, 2018. — 576 p. — ISBN: 978-1788621113. Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their...
Packt Publishing, 2017. — 376 p. — ISBN: 978-1788398435. Extract patterns and knowledge from your data in easy way using MatLAB. Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MatLAB Understand how your data works and identify...
The MIT Press – 2012, 432 pages ISBN: 026201825X, 9780262018258 This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and...
StatQuest, 2022. — 305 p. — ISBN 979-8811583607. Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each...
Springer, 2016. — 364 p. Data science is one of the emerging fields in the twenty-first century. This field has been created to address the big data problems encountered in the day-to-day operations of many industries, including financial sectors, academic institutions, information technology divisions, health care companies, and government organizations. One of the important...
Пер. с англ. А. Логунов. — СПб.: БХВ-Петербург, 2022. — 640 с.: ил. — ISBN 978-5-9775-6763-3. Представлены фундаментальные знания и практические инструменты в области машинного обучения, в том числе более 100 углубленных упражнений на языке Python. Дано введение в машинное обучение и математическую оптимизацию, включая методы первого и второго порядков, градиентного спуска и...
CreateSpace Independent Publishing, 2017. — 490 p. — ISBN/ASIN: B06XC21FZV. Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and...
Manning, 2016. — 427 p. — ISBN: 1617291927, 9781617291920 Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling,...
Boston: The MIT Press, 2015. - 624p.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the...
2nd Edition. — MIT Press, 2010. — 581 p. Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. One case where learning is necessary is when human expertise does not exist, or...
ORM, 2012. - 304 p. - ISBN 1449303714 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on...
Packt Publishing, 2013. — 396 p. — ISBN: 1782162143, 9781782162148 Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never...
СПб.: Питер, 2017. — 336 с. — (Библиотека программиста). — ISBN: 978-5-496-02989-6. Данная книга рассчитана на тех, кто хочет решать самые разнообразные задачи при помощи машинного обучения. Как правило, для этого нужен Python, поэтому в примерах кода используется этот язык, а также библиотеки pandas и scikit-learn. Вы познакомитесь с основными понятиями ML, такими как сбор...
М.: МАКС Пресс, 2018. — 90 с. Книга представляет собой введение в основные понятия, методы и алгоритмы машинного обучения, которое находится в настоящее время в состоянии исключительно бурного развития и является теоретической основой для проектирования интеллектуальных систем обработки больших данных. В первой части книги излагаются элементарные аспекты машинного обучения:...
Machine Learning Mastery, 2018. — 115 р. Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own...
Packt Publishing, 2016. — 455 p. — ISBN: 9781783555130 We live in the midst of a data deluge. According to recent estimates, 2.5 quintillion (1018) bytes of data are generated on a daily basis. This is so much data that over 90 percent of the information that we store nowadays was generated in the past decade alone. Unfortunately, most of this information cannot be used by...
Packt Publishing, 2018. — 566 p. — ISBN: 1788390040. Practical, hands-on solutions in Python to overcome any problem in Machine Learning Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to...
MIT Press, 2015. — 599 p. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most...
The MIT Press, 2022. — 855 p. — ISBN 0262046822. Book draft from 9.5.2022 (book is officially published in March, 2022 !) A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning)...
InTech, 2010. — 446 p. The goal of this book is to present the key algorithms, theory and applications that from the core of machine learning. Learning is a fundamental activity. It is the process of constructing a model from complex world. And it is also the prerequisite for the performance of any new activity and, later, for the improvement in this performance. Machine...
М.: Диалектика, 2020. — 768 с. В книге излагаются основы статистического обучения для решения практических задач, возникающих в медицине, биологии, финансах и многих других отраслях науки и промышленности. В частности, рассматриваются основные понятия и методы статистического обучения: линейная регрессия, нелинейная регрессия, линейные методы классификации, регуляризация,...
М.: Грин Принт, 2019. — 140 с. Экономика относится к такой области деятельности, которая находится в постоянном развитии, поэтому возникающие в ней проблемы и задачи постоянно требуют новых решений. Именно поэтому на машинное обучение и искусственный интеллект экономисты и менеджеры возлагают огромные надежды, которые во многом уже сбываются. Книга посвящена методам машинного...
Packt Publishing, 2020. — 1470 p. Machine Learning for Trading. Market and Fundamental Data. Alternative Data for Finance. Alpha Factor Research. Strategy Evaluation. The Machine Learning Process. Linear Models. Time Series Models. Bayesian Machine Learning. Decision Trees and Random Forests. Gradient Boosting Machines. Unsupervised Learning. Working with Text Data. Topic...
O’Reilly Media, Inc., 2020. — 366 p. — ISBN: 978-1-492-05319-4. Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques...
Massachusetts Institute of Technology, 2002. — 645 p. — ISBN: 0262194759, 978-0262194754. Series: Adaptive Computation and Machine Learning series. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
СПб.: БХВ-Петербург, 2020. — 559 с. Книга посвящена практике применения машинного обучения с целью создания мощных алгоритмических стратегий для успешной торговли на финансовых рынках. Изложены базовые принципы работы с данными: оценивание наборов данных, доступ к данным через API на языке Python, доступ к финансовым данным на платформе Quandl и управление ошибками...
Manning Publications, 2025. — 373 p. — ISBN 1633438759. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a...
Apress, 2019. — 384 p. Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance,...
М.: МЦНМО, 2013. — 390 с. Предназначено для первоначального знакомства с математическими основами современной теории машинного обучения (Machine Learning) и теории игр на предсказания. В первой части излагаются основы статистической теории машинного обучения, рассматриваются задачи классификации и регрессии с опорными векторами, теория обобщения и алгоритмы построения разделяющих...
Издание автора, 2022. — 480 с.: 433 ил. — ISBN 978-5-04-511081-5. Эта книга будет интересна всем, кто хочет научиться применять Python 3 при разработке в области искусственного интеллекта и машинного обучения. С этой книгой Вы познакомитесь с основными терминами и понятиями ИИ, такими как машинное обучение, глубокое обучение и нейронные сети. Научитесь создавать чат-ботов с...
Packt Publishing, 2017. — 449 p. — ISBN: 978-1785889622. True PDF Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in...
Packt Publishing, 2013. — 290 p. — ISBN: 1782161406, 9781782161400. Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning...
Machine Learning Mastery, 2020. — 398 p. Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations,...
Tel-Aviv: Slomo Kashani, 2020. — 410 p. — ISBN 1916243568. Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning M.Sc./Ph.D. students, and those awaiting an interview a well-organized overview of the field. The problems it poses...
М.: Московский государственный университет (МГУ) имени М.В. Ломоносова, Teach-in, 2022. — 173 с. Введение. Задачи, подходы и возможности. Предмет изучения: искусственный интеллект и машинное обучение. Постановка задачи МО. Классификация задач МО. Глубокое МО - построение признаков. Модель нейрона Мак-Каллока-Питтса. Линейная регрессия и классификация. Построение моделей. Прямые...
СПб.: Питер, 2018. — 585 c. — ISBN: 978-5-496-03068-7. Книга "Python Data Science Handbook" - это подробное руководство по самым разным вычислительным и статистическим методам, без которых немыслима любая интенсивная обработка данных, научные исследования и передовые разработки.
AI Sciences LLC, 2018. — 132 р. — ASIN B07GMVP3WP. Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the...
СПб.: БХВ-Петербург, 2022. — 450 с. Приводимые в книге паттерны проектирования отражают лучшие практические подходы к решению типичных задач машинного обучения. Указанные паттерны, реализованные в программном коде, сконцентрировали опыт сотен экспертов в простые и легкодоступные советы. Книга содержит подробный разбор 30 паттернов, служащих для представления данных и задач,...
Andriy Burkov, 2019. — 160 p. — ISBN: 978-1-9995795-0-0. Update 2019-07-05 Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful...
New York: John Wiley & Sons, 2019. — 350 p. — ISBN: 1119548217. Cyber-solutions to real-world business problems! Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe....
Packt Publishing, 2020. — 530 p. Build, train, and deploy end-to-end machine learning and deep learning pipelines C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with...
AI Publishing LLC, 2020. — 301 p. — ISBN B08LSLHBR8. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce,...
Morgan Kaufmann Publishers, 2016. — 524 p. — ISBN: 9780128021217 Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech...
Apress, 2018. - 362p. - ISBN: 1484235630 Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a...
CRC Press, 2010. — 479 p. — ISBN 9781439815915. Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian...
СПб.: Питер, 2019. — 432 с.: ил. — (IT для бизнеса). — ISBN 978-5-4461-1154-1. Маркос Лопез де Прадо делится тем, что обычно скрывают — самыми прибыльными алгоритмами машинного обучения, которые он использовал на протяжении двух десятилетий, чтобы управлять большими пулами средств самых требовательных инвесторов. Машинное обучение меняет практически каждый аспект нашей жизни,...
Apress, 2018. - 510p. - ISBN: 978-1484238721 Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book—Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an...
Amazon Digital Services LLC, 2019. — 175 p. Python with machine learning is increasing day-to-day popularity. Python is currently a widely used programming language for machine learning. Like the use of python in professional work, it is also growing reputation in the academic sector. In Coursera, various online courses, like programming language for machine learning, the use...
СПб.: Питер, 2019. — 304 с. — (Для профессионалов). — ISBN: 978-5-4461-1061-2. Искусственный интеллект - это мощный инструмент в руках современного архитектора, разработчика и аналитика. Облачные технологии - ваш путь к укрощению искусственного интеллекта. Тщательно изучив эту незаменимую книгу от Ноя Гифта, легендарного эксперта по языку Python, вы легко научитесь писать...
Астана: Фолиант, 2024. — 688 с. — (Взгляд экспертов). — ISBN: 978-601-11-0034-2. Предисловие: Дмитро Джулгаков, ведущий специалист PyTorch Core. Исчерпывающее руководство по машинному (МО) и глубокому обучению с использованием языка программирования Python, фреймворка PyTorch и библиотеки scikit-learn. Рассмотрены основы МО, алгоритмы для задач классификации, классификаторы на...
AI Publishing, 2020. — 308 p. — ISBN B08QJMNVCX. 10 Machine Learning Projects Explained from Scratch Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful...
Wiley, 2019. — 307 p. — ISBN: 978-1-119-54567-5. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines....
Massachusetts Institute of Technology, Neural Information Processing Series, 2012. — 512 p. ISBN: 026201646X, 978-0262016469. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from...
Cambridge University Press, 2020. — 152 p. — ISBN: 1108792898. Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this...
MIT Press, 2012. — 1098 p. With the ever increasing amounts of data in electronic form, the need for automated methods for data analysis continues to grow. The goal of machine learning is to develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest. Machine learning is thus closely...
4th ed. — The MIT Press, 2020. — 712 p. — ISBN 9780262358064. A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new...
O’Reilly, 2020. — 260 p. — ISBN: 149204511X. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the...
Packt Publishing, 2019. — 456 p. — ISBN: 1789136364. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind...
Springer, 2010. — 736 p. Machine learning (ML) is one of the most fruitful fields of research currently, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems. From a technological point of view, the world has changed at an unexpected pace; one of the consequences is that it is possible to use high-quality and fast...
InTech, 2009. — 430 p. Machine Learning is often referred to as a branch of artificial intelligence which deals with the design and the development of algorithms and techniques that help machines to learn. Hence, it is closely related to various scientific domains as Optimization, Vision, Robotic and Control, Theoretical Computer Science, etc. Based on this, Machine Learning...
Boston: The MIT Press, 2021. — 863 p. A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered...
2nd Edition. — Academic Press, 2020. — 1160 p. — ISBN: 9780128188033. This book, Second Edition, gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches based on optimization techniques combined with the Bayesian inference approach. The book builds from the basic classical methods to recent trends, making it suitable for...
2nd ed. — Packt Publishing, 2015. — 305 p. — ISBN: 9781784392772 Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of...
2nd ed. — O’Reilly Media, 2023. — 453 p. — ISBN 1098134184. Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to create impressive generative deep learning models from scratch using Tensorflow and Keras, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers,...
Apress, 2017. — 204 p. Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time...
Abiprod Pty, 2018. — 125 p. — ISBN: 1718095031. Are you excited about Artificial Intelligence and want to get started?Are you excited about Machine Learning and want to learn how to implement in Python? The book below is the answer. Given the large amounts of data we use everyday; whether it is in the web, supermarkets, social media etc. analysis of data has become integral to...
Addison-Wesley Professional, 2019. — 588 p. — (Addison-Wesley Data & Analytics Series). — ISBN13: 978-0-13-484562-3. Полное руководство для начинающих по изучению и созданию систем машинного обучения с использованием Python. Книга "Машинное обучение с Python для всех" поможет вам освоить процессы, шаблоны и стратегии, необходимые для построения эффективных систем обучения, даже...
Пер. с англ. канд. хим. наук А.Г. Глузикевича, под ред. В.Р. Гинзбурга. — СПб..: Диалектика, 2020. — 432 с.: ил. — ISBN 978-5-907144-99-6. По мнению многих отраслевых экспертов, обучение без учителя — передовой рубеж технологий искусственного интеллекта (ИИ) и, возможно, ключ к созданию сильного ИИ. Поскольку подавляющая часть накопленных в мире данных не размечена, к ним...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN 978-1-492-08525-6. • 2021-12-07: First Release Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning...
Пер. Е. Матвеева. — СПб.: Питер, 2024. — 320 с. — (Библиотека программиста). — ISBN 978-5-4461-2130-4. Собеседования по проектированию системы машинного обучения - самые сложные из всех вопросов технического собеседования. Эта книга предоставляет надежную стратегию и базу знаний для решения широкого круга вопросов проектирования систем машинного обучения. Пошаговый подход...
Apress, 2019. — 702 p. — ISBN: 1484244699. Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform....
2nd edition. — Boston: The MIT Press, 2020. — 853 p. — ISBN 0262044692. The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics...
O’Reilly Media, 2017. — 201 р. — ISBN: 978-1491924136 True PDF Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with...
Apress, 2017. — 335 p. — ISBN: 978-1-4842-2249-2. This book is a comprehensive guide to machine learning with worked examples in MatLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially...
AI Sciences LLC, 2018. — 184 p. — ASIN B07G4M16JF. Are you thinking of learning more about Machine Learning using Python? This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you...
ISLAND: Manning Publications, 2022. — 578 p. — ISBN 9781617298714. Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production. Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You’ll adopt...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at...
Imperial College Press, 2014. — 260 p. — ISBN: 1848168136, 9781848168138 his volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is,...
Apress, 2019. — 347 p. — ISBN: 1484239156. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MatLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses...
Packt Publishing, 2017. — 270 p. Your one-stop guide to becoming a Machine Learning expert. Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by...
InTech, 2010, — 374 p. The purpose of this book is to provide an up-to-data and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call Learning tasks, as we use the word in daily life. It is also broad enough to encompass computer that improve from experience in quite...
Пер. с англ. С. Черников. — СПб.: Питер, 2023. — 256 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1773-4. Освойте ключевые навыки проектирования, разработки и развертывания приложений на базе машинного обучения (МО)! Пошаговое руководство по созданию МО-приложений с упором на практику: для специалистов по обработке данных, разработчиков программного обеспечения и...
Abhishek Thakur, 2020. - 300p. - ISBN: 8269211508 This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you...
Packt Publishing, 2015. — 405 p. — ISBN13: 978-1783982042. About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset from the churn dataset,applying different classification methods. Who This Book Is For If you want to learn how to use R for...
CreateSpace Independent Publishing Platform, 2013. — 520 p. — ISBN: 978-1489507716. This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with...
O’Reilly, 2018. - 226p. - ISBN: 1491976446 Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, youll first start to learn with regression modelling and then move into more advanced...
O’Reilly Media, 2016. — 300 p. — ISBN10: 149196460X, ISBN13: 978-1491964606. Machine learning has finally come of age. With H 2 O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H 2 0 with only...
Manning Publications Co, 2021. — 493 p. — ISBN 9781617295645. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs....
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
BPB Publications, 2018. — 266 p. — ISBN: 9386551934. This book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the...
Panda Ghana Publishing, 2019. — 246 p. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning Prologue General Matters K...
Учебное пособие. — Ульяновск: УлГТУ, 2017. — 290 с. — ISBN 978-5-9795-1712-4. Учебное пособие рассматривает вопросы, связанные с анализом данных: модели, алгоритмы, методы и их реализацию на языке Python. Особое внимание уделено анализу временных рядов. Книга предназначена для студентов группы направлений 09, а также для студентов других групп направлений, изучающих дисциплины,...
2 изд., перераб. и доп. — Астана: АЛИСТ, 2024. — 448 с.: ил. — ISBN 978-601-08-4119-2. Книга содержит около 200 задач машинного обучения, таких как загрузка и обработка текстовых или числовых данных, отбор модели и многие другие. Рассмотрена работа с языком Python, библиотеками pandas и scikit-learn. Коды примеров можно вставлять, объединять и адаптировать, создавая собственное...
Gulf Professional Publishing, 2021. — 461 p. — ISBN 978-0128219294. Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves...
Second updated edition. — O’Reilly, 2019. — 856 p. — ISBN: 1492032646. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using...
Early Release. — 2nd edition. — O’Reilly, 2019. — 510 p. — ISBN: 978-1-492-03264-9. Concepts, Tools, and Techniques to Build Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
Manning Publishing, 2022. - 338p. - ISBN: 1617298050 Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input. Optimize every stage of your machine learning pipelines with powerful automation components...
Leanpub, 2019. — 259 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning. This book is written for you, the Machine Learning practitioner . Every chapter describes a problem and a solution that you'll encounter in your Machine Learning...
2-е издание. — СПб.: Диалектика, 2020. — 1040 с. — ISBN 978-5-907203-33-4. Благодаря серии выдающихся достижений глубокое обучение значительно усилило всю область машинного обучения. В наше время даже программисты, почти ничего не знающие об этой технологии, могут использовать простые и эффективные инструменты для реализации программ, которые способны обучаться на данных. Новое...
Cambridge University Press, 2011, 492 pages, 144 b/w illus. ISBN9780521192248. This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset...
Academic Press, 2020. - 534p. - ISBN: 9780128213797 Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with...
Пер. с англ. — СПб: БХВ-Петербург, 2019. — 384 с.: ил. — ISBN: 978-5-9775-4056-8. Книга содержит около 200 рецептов решения практических задач машинного обучения, таких как загрузка и обработка текстовых или числовых данных, отбор модели, уменьшение размерности и многие другие. Рассмотрена работа с языком Python и его библиотеками, в том числе pandas и scikit-leam. Решения всех...
2nd ed. — Packt Publishing, 2018. — 552 p. — ISBN: 1789347998. An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions...
Apress, 2019. - 365p. - ISBN: 9781484236574 Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior...
MIT, 2016. — 206 p. — ISBN: 9780262529518 Alpaydın Ethem Machine Learning: The New AI Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect...
Wiley, 2021. — 216 p. — ISBN 978111968236. Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series...
Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli. — Apress Media LLC., 2020. — 320 p. — ISBN13: 978-1-4842-6093-7. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the...
2nd Edition. — Cambridge University Press, 2020. — 594 p. — ISBN: 978-1-108-48072-7. With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all...
InTech, 2010. — 288 p. In recent years many successful machine learning applications have been developed, ranging from data mining programs that learn to detect fraudulent credit card transactions, to information filtering systems that learn user’s reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, machine learning techniques...
Учебное пособие. — СПб: Университет ИТМО, 2020. — 40 с. В учебном пособии изложены методы автоматизации машинного обучения: методы конструирования признаков, выбора моделей обучения и оптимизации гиперпараметров алгоритмов, оценки результатов обучения, а также актуальные проблемы. Учебное пособие предназначено для студентов, изучающих курс «Методы машинного обучения» по...
Монография. — Симферополь: Диайпи, 2014. — 228 с. — ISBN: 978–966–491–534–9. В книге рассматриваются теоретические аспекты машинного обучения классификации. В центре изложения — обучаемость как способность применяемых алгоритмов обеспечивать эмпирическое обобщение. С обучаемостью непосредственно связаны вопросы сложности выборок, точности и надежности классификаторов. Большое...
Wiley, 2014. — 408 p. — ISBN: 1118889061 Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML...
Manning Publications, 2021. — 502 p. — ISBN 9781617296819. In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll...
CRC Press, 2022. — 283 p. — ISBN: .978-1-003-17008-2. Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning:...
Астана: Фолиант, 2023. — 364 с. Сегодня машинное обучение — основной практический аппарат для применения искусственного интеллекта. Книга, основанная на стэнфордском учебном курсе, не ограничивается разбором конкретных библиотек, а описывает высокоуровневый подход к разработке систем машинного обучения, который упрощает их поддержку и развитие, позволяет избежать...
Globaltech NTC, 2018. — 144 р. You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been...
Packt Publishing, 2016. — 941 p. — ISBN13: 978-1-78712-321-2. Learn to solve challenging data science problems by building powerful machine learning models using Python. Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is...
Leanpub, Sebastian Raschka, 2023. — 231 p. Have you recently completed a Machine Learning or Deep Learning course and wondered what to learn next? With 30 questions and answers on key concepts in Machine Learning and AI, this book provides bite-sized bits of knowledge for your journey to becoming a Machine Learning expert. Expand Your Machine Learning Knowledge Machine Learning...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Final version ! Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of...
Manning Publications, 2018. — 275 p. — ISBN: 9781617293337. — MEAP version 11 Manning Early Access Program (MEAP). MEAP began February 2016. Publication in February 2018 (estimated). Reactive Machine Learning Systems teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. This example-rich guide...
Springer, 2008. — 610 p. The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a unified style. In a nutshell, we identify at least...
CRC Press, 2020. - 488p. - ISBN: 9781138495685 Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which...
Third Edition. — 2018. — 442 p. This book implements many common Machine Learning algorithms in equivalent R and Python. This is the 3rd edition of the book. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques...
Independently published, 2019. - 435 p. - ISBN: 1710263423. Created with the beginner in mind , this powerful bundle delves into the fundamentals behind Python and Machine Learning , from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you’ll discover everything you need to know to get started with Python and Machine Learning, and...
AI Sciences, 2018. — 190 p. — ISBN13: 978-1724417503. This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests...
2nd. ed. — Springer-Verlag London, 2019. — 995 p. — ISBN: 1447174518. This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and...
Packt Publishing, 2019. — 456 p. — ISBN: 1789136364. Code files only! A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and...
Apress, 2021. — 192 p. — ISBN 978-1484271094. Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern...
Packt Publishing, 2017. — 570 p. — ISBN: 9781785280511. Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a...
Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko. — O’Reilly Media, Inc., 2020. — 264 p. — ISBN: 978-1-492-05012-4. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data...
New York: Morgan & Claypool, 2021. — 161 p. Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into Deep Learning (DL) architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge...
2018. — 246 p. — ISBN: 1727338944. Have you ever wanted to learn how to better use your data? Are you interested in the works of machine learning? If you answered yes to these questions, then this book is for you. Machine Learning and Deep learning are powerful data tools that can help improve businesses. In this book, you will learn: Neural networks Machine learning Python and...
Independently published, 2021. — 674 p. — ISBN B08V4VZSCH. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine...
O’Reilly Media, Inc., 2021. — 481 p. — ISBN 9781098102364. By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection,...
John Wiley & Sons, 2021. — 464 p. — (For Dummies). 2nd Edition Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android?as in the movie Ex Machina?it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving...
Packt, 2016. — 358 p. — ISBN: 978-1-78588-840-3. Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning...
Cambridge University Press, 2020. — 287 p. When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to...
Data Sciences, 2017. — 136 p. — ISBN: 1985670968. The overall aim of this book is to help you understand the application of machine learning techniques with Python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what...
O’Reilly, 2019. — 320 p. — ISBN: 1492047546. Matt Harrison, "Machine Learning Pocket Reference: Working with Structured Data in Python" With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a...
Manning Publications, 2024. — 328 p. — ISBN-13: 978-1633439214. Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical...
М.: ДМК Пресс, 2022. — 308 c. — ISBN 9785937001252, 5937001257. Книга представляет собой подробный обзор передовых практик и паттернов проектирования в области прикладного машинного обучения. В отличие от многих учебников, уделяется внимание инженерным аспектам МО. Рассматриваются сбор, хранение и предобработка данных, конструирование признаков, а также тестирование и отладка...
Издание автора, 2024. — 432 с. Исследуйте мир машинного обучения с этой книгой, предназначенной для тех, кто стремится погрузиться в фундаментальные принципы и передовые методы этой динамично развивающейся области. От введения в основные концепции до глубокого погружения в продвинутые техники и приложения, каждая глава представляет собой комплексное исследование, подкрепленное...
2nd ed. — Birmingham: Packt Publishing, 2018. — 513 p. — ISBN: 1789347998. Key Features Explore statistics and complex mathematics for data-intensive applications. Discover new developments in EM algorithm, PCA, and bayesian regression. Study patterns and make predictions across various datasets. Book Description Machine learning has gained tremendous popularity for its...
Packt Publishing, 2022. — 552 p. — ISBN 9781803246802. We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly...
Apress, 2018. — 355 p. — ISBN: 978-1-4842-2987-3. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the...
O'Relly, 2021. — 156 р. — ISBN: 978-1098115715. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the...
O’Reilly Media, Inc., 2020. — 408 p. — ISBN: 978-1-098-11578-4. Code files only! The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of...
Packt Publishing, 2016. — 439 p. — ISBN: 9781785887215 Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and...
LazyProgrammer, 2016. — 66 p. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Think about the large amounts of data being collected...
Packt Publishing, 2020. — 740 p. — ISBN: 978-1838820299 2nd.ed. Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time...
Amazon.com Services LLC., 2020. — 146 p. Become the master of machine learning with this powerful guide. Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin? Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple,...
Packt, 2019. — 360 p. — ISBN: 9781838645359. This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. Key Features Roll up your sleeves and start programming AI models No math, data science, or machine learning background required Packed with hands-on examples, illustrations, and clear step-by-step instructions 5...
Birmingham: Packt Publishing, 2019. — 199 p. — ISBN: 1789345804. Key Features Use Q-learning to train deep learning models using Markov decision processes (MDPs). Study practical deep reinforcement learning using deep Q-networks. Explore state-based unsupervised learning for machine learning models. Book Description Q-learning is a machine learning algorithm used to solve...
2nd ed. — School of Management University of Toronto, 2020. — 274 p. — ISBN: 9798644074372. This book is for business executives and students who want to learn about the tools used in machine learning. In creating the second edition, John Hull has continued to improve his material and added three new chapters. The book explains the most popular algorithms clearly and succinctly...
Packt Publishing, 2018. — 350 p. — ISBN: 1788996402. Power up your C# and .NET applications with exciting machine learning models and modular projects Key Features Produce classification, regression, association and clustering models Expand your understanding of machine learning and C# Get the grips of C# packages such as Accord.net, LiveCharts, Deedle Book Description Machine...
Ethem Mining, 2020. — 177 p. What is Machine Learning? Definition of Machine Learning History of Machine Learning The Future of Machine Learning Application of Machine Learning Technology Industry Agricultural Industry Medical Industry Financial Industry Marketing Industry Human Behavior Industry Benefits of Machine Learning Practical Examples of Everyday Use of Machine...
2nd edition. — Boston: The MIT Press, 2018. — 120 р. — (Adaptive Computation and Machine Learning series). A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers...
K.Taylor, 2017. — 413 p. Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases....
2018. — 75 p. How can a beginner approach machine learning with Python from scratch? Why exactly is machine learning such a hot topic right now in the business world? Ahmed Ph. Abbasi will lead you from being a complete beginner in learning a sound method of data analysis that uses algorithms, which learn from data and produce actionable and valuable information. The basis for...
MIT Press, 2017. — 225 p. — (MIT Press essential knowledge). — ISBN: 9780262529518. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we dont yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data...
Second Edition. — Wiley, 2020. — 356 p. — ISBN: 978-1-119-56201-6. This book, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark...
InTech, 2009. — 386 p. Even since computers were invented many decades ago, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both...
2nd Edition. — O’Reilly, 2023. — 413 p. — ISBN 978-1-098-13572-0. This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models...
O’Reilly, 2017. — 1139 p. — ISBN: 978-1-491-96229-9. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples,...
Springer, 2019. — 219 p. — ISBN: 3030053172. This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid...
Packt Publishing, 2016. — 252 p. — ISBN: 978-1-78588-295-1. Design efficient machine learning systems that give you more accurate results. Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of...
Leanpub, 2022. — 111 p. The underlying goal of "Machine Learning Simplified" is to develop strong intuition for ML inside you. We would use simple intuitive examples to explain complex concepts, algorithms or methods, as well as democratize all mathematics behind Machine Learning (ML). After reading this book, you would understand everything that comes into the scope of...
Пер. с англ. — СПб.: БХВ-Петербург, 2024. — 448 с.: ил. — (От профессионалов для профессионалов). — ISBN: 978-5-9775-1903-8. Книга рассказывает о продвинутых приёмах машинного обучения и науки о данных (data science) на основе задач, решаемых на всемирно известной платформе Kaggle. Показано (в том числе на примере увлекательных интервью с Kaggle-гроссмейстерами), как устроена...
Springer, 2008. — 483 p. One of the most interesting technological phenomena in recent years is the diffusion of consumer electronic products with constantly increasing acquisition, storage and processing power. As an example, consider the evolution of digital cameras: the first models available in the market in the early nineties produced images composed of 1.6 million pixels...
2nd edition. — Morgan & Claypool, 2018. — 207 p. — ISBN: 978-1681733043. Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a...
Packt Publishing, 2017. — 442 p. — ISBN: 9781788295758. Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on...
2nd Edition. (3rd. Rev.) — O’Reilly, 2019. — 856 р. - ISBN: 1492032646 Final Edition Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this...
2nd ed. — Birmingham: Packt Publishing, 2024. — 655 p. — ISBN 1835883184. Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures. Key Features Apply ML and global models to improve forecasting accuracy through...
Springer, 2018. — 373 p. — ISBN: 3319949888. This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental...
Springer, 1983. — 565. The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learning processes is of great significance to fields concerned with understanding intelligence. Such fields include cognitive science, artificial intelligence, information science, pattern recognition,...
Oliver Theobald, 2017. — 52 p. The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the...
ITexLi, 2017. — 446 p. — ISBN: 953-307-033-1. The goal of this book is to present the key algorithms, theory and applications that from the core of machine learning. Learning is a fundamental activity. It is the process of constructing a model from complex world. And it is also the prerequisite for the performance of any new activity and, later, for the improvement in this...
O’Reilly, 2015. — 59 p. Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O’Reilly report, machine-learning expert Alice Zheng takes you through...
Учебное пособие. — Ростов-на-Дону; Таганрог: Южный федеральный университет, 2020. — 97 с. — ISBN 978-5-9275-3405-0. В учебном пособии освещается теоретический материал по курсу «Интеллектуальный анализ данных в научных исследованиях», приведены примеры решения практических задач и лабораторных работ. Пособие освещают методы кластеризации, вопросы построения рекомендательных...
Елена Капаца, 2023. — 65 с. Краткий гайд для новичков по машинному и глубокому обучению с разбором кода. Здесь вы найдете необходимый минимум по предмету, истолкованный языком, понятным школьнику. Некоторые разделы написаны с помощью ChatGPT. По прочтении вы избавитесь от страха перед технологией и освоите базовый инструментарий подготовки данных, их загрузке в модель и ее...
Учебное пособие. — Екатеринбург: Уральский федеральный университет им. Первого президента России Б.Н. Ельцина (УрФУ), 2020. — 88 с. — ISBN: 978-5-7996-3015-7. Изложены основы машинного обучения, а также история его появления. Даны определения основным понятиям: выборка, объекты выборки, параметры, функционал ошибки и прочее. Описаны основы градиентного спуска и его модификаций,...
Packt Publishing, 2016. — 941 p. — ISBN13: 978-1-78712-321-2. Learn to solve challenging data science problems by building powerful machine learning models using Python. Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is...
Wiley, 2018. — 352 p. — ISBN: 1119439191. An introduction to machine learning that includes the fundamental techniques, methods, and applications. Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author an expert in the field presents fundamental ideas, terminology, and...
CreateSpace Independent Publishing Platform, 2017. — 56 p. The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple.From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept that...
Manning Publications Co., 2019. — 280 p. — ISBN: 978-1617293702. Code files only! MEAP Version 7 It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using...
Springer, 2016. — 345 p. — ISBN: 978-3-319-17289-7, e-ISBN: 978-3-319-17290-3. Machine learning stands as an important research area that aims at developing computational methods capable of improving their performances with previously acquired experiences. Although a large amount of machine learning techniques has been proposed and successfully applied in real systems, there...
Routledge, 2021. — 259 p. — (Routledge Advanced Texts in Economics and Finance). — ISBN 978-0-367-48081-3. This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price...
3rd Edition. – O’Reilly Media, 2023. –- ISBN 978-1-098-12597-4. Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal...
O’Reilly Media, 2018. — 45 p. — ISBN: 9781492033158. Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting...
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
Packt Publishing, 2016. — 278 p. — ISBN: 978-1-78439-863-7. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum...
Wiley, 2020. — 460 p. — ISBN: 1786305445. …This groundbreaking book on Machine Learning for Asset Management represents a refreshing collaborative effort between sophisticated investment practitioners and researchers, to present practical application of machine learning methodologies. As one can see from the different chapters, machine learning can be applied to different parts...
O’Reilly Media, 2023. - 265 p. - ISBN 1492097675. There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual...
Independently published, 2023. — 245 p. In the ever-changing world of finance and trading, the search for a competitive edge has been a constant driver of innovation. Over the last few decades, the field of quantitative trading has emerged as a powerful force, pushing the boundaries of what is possible and reshaping the way we approach the market. At the heart of this...
Apress, 2017. — 382 p. — ISBN10: 1484222490, 13 978-1484222492 This book is a comprehensive guide to machine learning with worked examples in MatLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews...
Packt Publishing, 2016. — 1043 p. — ISBN: 978-1-78712-857-6. Leverage benefits of machine learning techniques using Python Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine...
3rd ed. — CRC Press, 2017. — 691 p. — ISBN 1498797601. The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. is a compilation of new and creative data mining techniques,...
Manning Publications, 2018. — 253 p. Machine learning applications autonomously reason about data at massive scale. It’s important that they remain responsive in the face of failure and changes in load. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. teaches readers how to implement reactive...
LazyProgrammer, 2016. — 66 p. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Think about the large amounts of data being collected...
СПб.: Питер, 2023. — 496 с. Изучите ключевые концепции машинного обучения‚ работая над реальными проектами! Машинное обучение - то, что поможет вам в анализе поведения клиентов, прогнозировании тенденций движения цен, оценке рисков и многом другом. Чтобы освоить машинное обучение, вам нужны отличные примеры, четкие объяснения и много практики. В книге все это есть! Автор...
2nd Edition. — Manning Publications, 2022. — 568 p. Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python . All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages...
New York: Chapman and Hall/CRC, 2019. — 335 c. Machine Learning is known under many names such as Machine Learning, Artificial Intelligence, Pattern Recognition, Data Mining, Data Assimilation, and Big Data, to list but a few. It developed in many areas of science, such as in physics, engineering, computer science, and mathematics in parallel and independently. For example it...
Second Edition. — Independently published, 2021. — 281 p. — ISBN 978-1838132217. This Second Edition improves clarity of code, general formatting, text explanations as well as updating the AI for 2021 stock selection. Build Your Own AI Investor Breaks down Value Investing for the AI revolution, whilst being accessible to anyone, even if you've never invested in stocks or coded...
Springer, 2008. — 256 p. The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. With first papers dating back to the 1960’s, DAR is a mature but still growing research field with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely...
Self-published, 2021 — 675 p. — ISBN : 978-1-6641-5127-7. This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest...
Amazon Digital Services, 2017. — 61 p. This book is an introduction to basic machine learning and artificial intelligence. It gives you a list of applications, and also a few examples of the different types of machine learning. Here's What You'll Learn in this Book: – Introduction to Machine Learning – Different Applications of Machine Learning – Introduction to Statistics for...
Morgan & Claypool Publishers, 2019. — 175 p. — (Synthesis Lectures on Data Management). — ISBN10: 1681734982, 13 978-1681734989. Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many...
Jorge Brasil, 2023. — 151 p. Has the abstract nature of linear algebra ever left you overwhelmed? Do you yearn to unlock the essence of machine learning but are bogged down by the intricacy of mathematics? Dive into a realm where linear algebra unfolds not just as numerical operations, but as a powerful story. A story intertwined with the magic of machine learning, making sense...
The MIT Press, 2017. — 624 p. — ISBN: 026203641X. The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage,...
Birmingham: Packt Publishing, 2019. — 403 p. — ISBN: 1789954924. Explore the exciting world of machine learning with the fastest growing technology in the world ! Key Features Understand various machine learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into how you can use machine learning...
Boca Raton: Chapman & Hall/CRC Press, 2004. — 365 p. — (Computer science and data analysis). — ISBN 1-58488-387-1. Bayesian Artificial Intelligence, in our understanding, is the incorporation of Bayesian inferentialmethods in the development of a software architecture for an Artificial Intelligence (AI). We believe that important ingredients of such an architecture will be...
Apress, 2021. — 296 p. — ISBN 978-1-4842-7149-0. Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a...
Manning Publications, 2024. — 248 р. — ISBN: 978-1617299025. Practical patterns for scaling Machine Learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Construct machine learning pipelines with data ingestion, distributed...
СПб.: Питер, 2019. — 512 с.: ил. — (IT для бизнеса). — ISBN 978-5-4461-0926-5. Наука о данных становится неотъемлемой частью любой маркетинговой деятельности, и эта книга является живым портретом цифровых преобразований в маркетинге. Анализ данных и интеллектуальные алгоритмы позволяют автоматизировать трудоемкие маркетинговые задачи. Процесс принятия решений становится не...
IBM Limited Edition. - John Wiley & Sons, Inc., 2018. – 75 p. Machine learning is having a dramatic impact on the way software is designed so that it can keep pace with business change. Machine learning is so dramatic because it helps you use data to drive business rules and logic. How is this different? With traditional software development models, programmers wrote logic based...
O’Reilly Media, 2014. — 234 p. — ISBN: 1449374069, 9781449374068 Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms,...
Packt Publishing, 2018. — 378 p. — ISBN: 1788838297. Leverage the power of Apple's Core ML to create smart iOS apps. Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple, with APIs...
Apress, 2020. — 355 p. — ISBN: 1484256689. Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation...
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
Автор, 2023. — 110 с. Практическое руководство, предназначенное для всех, кто хочет войти в мир машинного обучения и освоить его основы. Авторы книги предлагают читателям увлекательное путешествие в эту захватывающую область, начиная с основных концепций и принципов машинного обучения и заканчивая практическими навыками построения и обучения моделей. Внутри книги читатели...
Учебное пособие. — Новосибирск: Новосибирский государственный технический университет, 2010. — 79 с. В учебном пособии излагаются основы теории и методов машинного обучения в вероятностной постановке. Под машинным обучением понимается анализ данных, при котором выявляются закономерности или строятся модели, описывающие данные. Дисциплина базируется на методах математической...
New York: Springer, 2019. — 433 p. This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as...
O’Reilly Media, 2024. — 307 p. — ISBN 1098146549. As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang...
Springer International Publishing, Switzerland, 2017. — 229 p. — (Fluid Mechanics and Its Applications. Volume 116) — ISBN10: 331940623X. Guides the reader from the control of simple dynamical systems to real-world experiments assisted by ample supplementary material Contains interviews with leading experts in the field Offers extensive color figures with clear explanations...
EGEA Spa - Bocconi University Press, 2020. — 200 p. — ISBN: 9788831322041. If you are looking for an engaging book, rich in learning features, which will guide you through the field of Machine Learning, this is it. This book is a modern, concise guide of the topic. It focuses on current ensemble and boosting methods, highlighting contemporray techniques such as XGBoost (2016),...
Springer, 2021. - 411p. - ISBN 9783030658991 This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine...
Издательство EPFL Press, 2009, -380 p. The book is devoted to the analysis, modelling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense, machine learning can be considered a subfield of artificial intelligence; the subject is mainly concerned with the development of techniques and algorithms that allow computers to learn from...
O’Reilly, 2020. — 408 p. — ISBN: 9781098115784. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into...
De Gruyter, 2019. — 537 p. — ISBN: 978-3-11-049950-6. This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artificial...
BPB Publications, 2021 — 341 p. — ISBN 9388511972. A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem. Key Features ● Develop a Conceptual and Mathematical understanding of Statistics ● Get an overview of Statistical Applications in Python ● Learn how to perform Hypothesis testing in Statistics ● Understand why Statistics...
СПб.: Питер, 2025. — 609 с.: ил. — (Библиотека программиста). — ISBN: 978-5-4461-4191-3. Машинное обучение давно уже стало синонимом искусственного интеллекта. Оно проникло во многие аспекты нашей жизни и стало одной из важнейших областей современной науки. Эта книга — путеводитель по ключевым идеям машинного обучения. Вы узнаете, как методы машинного обучения получаются из...
Красноярск: СГУ, 2019. — 161 с. Рассмотрены методы и модели машинного обучения для построения автоматизированных систем человеко-машинного взаимодействия. Предназначена для студентов, аспирантов и научных работников, интересующихся проблемами проектирования методов и моделей машинного и глубинного обучения.
O’Reilly, 2018. - 366p. - ISBN: 1491989386 This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data,...
Springer, 2019. — 494 p. — ISBN: 3030181138, 9783030181130. Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in...
CRC, 2020. — 525 p. — ISBN: 978-1138484696. The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from...
Amazon Digital Services LLC, 2019. — 135 р. — ASIN B07NRVL21R. There are different processes that are included within the meaning of the term "learning." If you refer to a dictionary and look up the meaning of learning, then you will come across different phrases like "to gain knowledge, understanding of or skill, through study, experience or instruction," and "the change in...
Cambridge University Press, 2022. — 420 p. — ISBN-10 1108837042; ISBN-13 978-1108837040. This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability, and statistics but no prior exposure to machine learning. Coverage...
Packt Publishing, 2017. — 420 p. — ISBN: 978-1-78728-074-8. Key Features Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Book Description Join...
Manning Publications, 2024. — 667 p. — ISBN 163343883X. Solve design, planning, and control problems using modern machine learning and AI techniques. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: Machine learning methods for search and optimization problems. The core concepts of search and optimization. Deterministic and...
2nd edition. — Springer, 2017. — 348 p. — ISBN: 978-3-319-63912-3. This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial...
Packt Publishing, 2017. — 344 p. — ISBN: 978-1783553112. +True PDF +Sample Files Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques...
New York: lulu.com, 2021. — 368 p. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like...
Manning Publications, 2021. — 426 p. — ISBN 9781617296741. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction...
The MIT Press, 2023. — 1354 p. An advanced book for researchers and graduate students working in Machine Learning and statistics who want to learn about Deep Learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate...
Independently published, 2021. — 201 p. — ISBN B08QGZJDHK. Master the world of machine learning and data science with this comprehensive beginner’s bundle. Data Science and Machine Learning are the biggest buzzwords in the business world nowadays. If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily – we have the...
Packt Publishing, 2024. — 603 p. — ISBN.978-1-80512-250-0. The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI, 2nd Edition / Справочник архитектора решений для машинного обучения: Практические стратегии и рекомендации по жизненному циклу ML, системному проектированию, MLOps и...
CreateSpace, 2017. — 266 p. Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Morgan & Claypool, 2018. — 169 p. The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many...
Independently published, 2020. — 335 p.— ISBN B08RCSJ39K. Mastering Deep Learning with Python: 2 Manuscripts This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and...
CRC Press, 2020. — 204 p. — ISBN13: 978-1-138-32822-8. In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have...
М.: 2013-2018. — 484 с. Предназначено для первоначального знакомства с математическими основами современной теории машинного обучения (Machine Learning) и теории игр на предсказания. В первой части излагаются основы статистической теории машинного обучения, рассматриваются задачи классификации и регрессии с опорными векторами, теория обобщения и алгоритмы построения разделяющих...
Apress Media LLC., 2020. — 262 p. — ISBN13: 978-1-4842-5771-5. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning...
2nd Edition. — Wiley, 2020. — 420 p. — ISBN: 978-1-119-64225-1. Dig deep into the data with a hands-on guide to machine learning with updated examples and more! This book provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant,...
Springer, 2020. — 366 p. — ISBN: 3030276554. Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. The book is intended to be concise but...
Independently published, 2019. — 182 p. — ISBN: 978-1-081-47400-3. Useful Python Tools eg. Anaconda, Jupiter Notebook Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model but you have no programming skill? Do you want to get started with learning data science? This audiobook is going to...
New Jersey: Wiley, 2009. - 267 p.
This book has been written to provide an introduction to this important class of machine learning algorithms with a minimum of technical background in order to make this material as widely accessible as possible. With the exception of some basic notions in calculus and probability theory, the book is completely self-contained. Important concepts...
O’Reilly Media, 2022. — 386 p. — ISBN-13: 978-1-098-10796-3. Machine learning systems are both complex and unique. They are complex because they consist of many different components and involve many different stakeholders. They are unique because they are data-dependent, and data varies wildly from one use case to the next. This book takes a holistic approach to designing...
Cambridge University Press, 2014. — 603 p. — ISBN 9781107024960. Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the...
CRC Press, 2019. — 349 p. На английском языке Промышленное применение машинного обучения показывает, как машинное обучение может применяться для решения реальных проблем в четвертой промышленной революции, и предоставляет необходимые знания и инструменты, позволяющие читателям создавать свои собственные решения на основе теории и практики. Книга знакомит с четвертой...
Cambridge: Cambridge University Press, 2020. — 537 p. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and...
New York: Sybex, 2019. — 510 p. Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not...
Princeton University Press, 2021 Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly...
Oakville (Canada): Arcler Press, 2019. — 347 p. — ISBN: 177407365X. Fundamentals of Machine Learning discusses the basics of Python, use of Python in computing and provides a general outlook on machine learning. This book provides an insight into concepts such as linear regression with one variable, linear algebra, and linear regression with multiple inputs . The classification...
Chapman and Hall/CRC, 2017. — 364 p. — (Machine Learning & Pattern Recognition). — ISBN10: 1138626783; ISBN13: 978-1138626782. Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove...
2nd Edition. — Apress, 2019. — 469 p. — ISBN13: 978-1-4842-4946-8. Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts:...
Springer, 2024. — 529 p. — ISBN 3031532813. This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of...
Apress Media LLC., 2020. — 177 p. — ISBN13: 978-1-4842-6578-9. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters...
New York: Springer, 2018. — 173 p. This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society....
Packt Publishing, 2016. — 298 p. — ISBN: 978-1-78588-660-7. — True PDF. Explore the web and make smarter predictions using Python. Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book...
Packt Publishing, 2016. — 491 p. — ASIN B01BSTEDKY. TRUE PDF Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring...
O’Reilly Media, Inc., 2023. — 250 p. Whether based on academic theories or discovered empirically by humans and machines, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. Unlike...
Wiley, 2012. — 298 p. There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic...
Manning Publications, 2023. — 354 p. — ISBN: 978-1-61729-713-7. Ensemble Machine Learning combines the power of multiple Machine Learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf...
Cham: Springer International Publishing, 2019. — 273 p. — ISBN: 978-3-319-98566-4. This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also...
Apress, 2021. — 284 p. — ISBN 978-1484270226. Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights)....
Springer, 2020. - 286 p. - (Algorithms for Intelligent Systems). - ISBN: 9813299894. This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction , it includes techniques such as support vector machines, extreme learning machines,...
Amazon Digital Services LLC, 2018. — 129 p. — ASIN B07F2NYDTH. Smart homes, self-driving cars, Siri, Alexa - some typical examples of how machine learning and artificial intelligence have become part of our daily life. Wouldn't it be cool to understand the concepts behind these complex topics? This book teaches you how to integrate machine learning into your apps. We're going...
Apress, 2019. — 368 р. — ISBN: 978-1-4842-3799-1. Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data...
Apress, 2017. — 358 p. — ISBN: 978-1484228654. Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six...
Reactive Publishing, 2024. — 553 p. — ISBN: 979-8874337056. Unlock the full potential of your financial analysis with "Machine Learning for Finance." This comprehensive guide takes you from the basics of Python programming to advanced Machine Learning techniques tailored specifically for financial applications. Perfect for finance professionals, data scientists, and anyone...
Пер. с англ. А. Логунов. — СПб.: БХВ-Петербург, 2022. — 640 с.: ил. — ISBN 978-5-9775-6763-3. Представлены фундаментальные знания и практические инструменты в области машинного обучения, в том числе более 100 углубленных упражнений на языке Python. Дано введение в машинное обучение и математическую оптимизацию, включая методы первого и второго порядков, градиентного спуска и...
Independently published, 2020. — 294 p. — ISBN B085PSKBRH. 3 Books in 1! Are you searching for the fastest way to discover the secrets of the fascinating world of Computer Science? For a very limited time, you have the opportunity to get three best-selling guides in a single phenomenal mega bundle: if you are a student or a professional looking for more technical skills, then...
Independently published, 2022. — 457 p. — ISBN-13 979-8437574973. Many market professionals consider unsupervised learning the following frontier in artificial intelligence, one that might hold the secret to the holy grail in AI study, the so called general expert system. Considering that the majority of the world’s data is unlabeled, traditional supervised knowing can not be...
Independently Published, 2020. — 107 p. Are you looking for an easy to follow guide for learning Python programming for Machine Learning? In this book Marvin Dale is going to teach you the basics and some advanced tricks for learning Python and Machine Learning. "Machine Learning is the future". You have surely heard that sentence, from the news or from a friend. You also have...
Orange Education Pvt Ltd, AVA, 2024. — 714 p. — ISBN 978-81-97651-20-5. Ultimate MLOps для моделей машинного обучения: используйте реальные практические примеры для эффективного создания, развертывания и масштабирования конвейеров машинного обучения с помощью MLOps The only MLOps guide you'll ever need. Key Features - Acquire a comprehensive understanding of the entire MLOps...
O’Reilly Media, 2022. — 99 p. — ISBN 9781098107956. Early release Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall...
Springer, 2006. — 656. Feature selection and model selection are two major elements in machine learning. Both feature selection and model selection are inherently multi-objective optimization problems where more than one objective has to be optimized. For example in feature selection, minimization of the number of features and maximization of feature quality are two common...
Packt Publishing, 2016. — 257 p. — ISBN10: 1784396583, 13 978-1784396589. If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be...
Springer, 2018. — 118 p. The ideas introduced in this book explore the relationships among big data, machine learning and granular computing. In many studies, machine learning has been considered as a powerful tool of big data processing. The relationship between big data and machine learning is very similar to the relationship between resources and human learning. In this...
Apress, 2021. — 192 p. — ISBN 1484265424, 9781484265420. Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application...
Amazon Digital Services LLC, 2018. — 54 р. — (Machine Learning for Beginners Book 1). Welcome to the world of machine learning! Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core...
Academic Press, 2019. — 449 p. — ISBN: 978-0-12-817444-9. This book presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive...
Apress, 2018. - 392p. - ISBN: 1484239504 Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You...
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning. — Morgan and Claypool Publishers, 2009. — 116 p. ISBN: 978-1598295474, e-ISBN: 978-1598295481. Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning...
Издание автора, 2024. --- 432 с. Исследуйте мир машинного обучения с этой книгой, предназначенной для тех, кто стремится погрузиться в фундаментальные принципы и передовые методы этой динамично развивающейся области. От введения в основные концепции до глубокого погружения в продвинутые техники и приложения, каждая глава представляет собой комплексное исследование,...
Пер. с англ. Логунов А. — СПб.: БХВ-Петербург, 2020. — 560 с. — ISBN 978-5-9775-6595-0. Книга посвящена практике применения машинного обучения с целью создания мощных алгоритмических стратегий для успешной торговли на финансовых рынках. Изложены базовые принципы работы с данными: оценивание наборов данных, доступ к данным через API на языке Python, доступ к финансовым данным на...
Jorge Brasil, 2023. — 314 p. Does the complexity of calculus in machine learning leave you feeling lost in a thicket of equations? Are you eager to find a guide that maps out this territory with clarity and ease? Enter a unique exploration where the world of calculus is demystified through the fascinating biology of bees, offering a perspective on mathematics that is as...
Mercury Learning & Information, 2020. — 339 p. — ISBN: 978-1-68392-467-8. This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures...
Springer, 2020. — 573 p. — ISBN: 3030410676. This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of...
Independently published, 2019. — 613 p. — ISBN B07Y2H2M8R. 4 Books in 1! Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model and you have no programming skill? Do you know a bit of Python coding and want to learn more about how this deep learning works? This bundle is going to guide you to...
Cambridge University Press, 2011. — 424 p. — ISBN: 0521196000, 9780521196000 The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines...
Gulf Professional Publishing, 2020. — 420 p. — ISBN: 978-0-12-817736-5. This book develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify...
Apress, 2020. — 446 p. — ISBN: 1484258010. Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from...
UK, King’s College London, 2018. — 237 p. This monograph is an attempt to offer a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing also more recent developments and pointers to the liter- ature for further study. This monograph aims at providing an introduction to key concepts, algorithms, and...
Independently publ., 2019. — 137 p. — ISBN13: 978-1097417117. Machine learning can be described as a form of statistical analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the...
Apress, 2020. - 258p. - ISBN: 9781484262214 Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load...
Apress, 2020. — 150 p. — ISBN 1484265459. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning...
Independently published, 2019. — 128 р. — ISBN: 1092554556. A baby learns to crawl, walk and then run. we are in the crawling stage when it comes to appllying Machine Learning. Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that makes...
Packt Publishing, 2024. — 398 p. Key Features: Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Book Description: Data labeling is the invisible hand that guides the power of artificial...
New York: Nova Science Publ., 2021. — 370 p. — (Computer Science, Technology and Applications). — ISBN 978-1-68507-0. Oснованная на алгоритмах глубокого обучения и ансамблевого обучения: практический подход с использованием Python Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning...
3rd edition. — Scatterplot Press, 2021. — 191 p. — ISBN B08RWBSKQB. Featured by Tableau as the first of “7 Books About Machine Learning for Beginners.” Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile? Well, hold on there… Before you embark on your journey, there are some high-level theory and...
2nd Edition. — Leanpub, 2020. — 272 p. This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery! Deep Learning has...
Packt Publishing, 2017. — 304 p. — ISBN: 1785882104. Build simple, maintainable, and easy to deploy machine learning applications The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but...
Учебное пособие. — МТУСИ. — М., 2018. — 83 с. Данное учебное пособие предназначено для студентов, обучающихся в магистратуре по направлению подготовки 27. 04. 04 - Управление в технических системах. Магистерская программа: "Интеллектуальный анализ данных в технических системах". Содержание учебного пособия соответствует основным вопросам, рассматриваемым в курсе "Machine...
Издание автора, 2022. — 480 с.: 433 ил. — ISBN 978-5045110815, 5045110810. Эта книга будет интересна всем, кто хочет научиться применять Python 3 при разработке в области искусственного интеллекта и машинного обучения. С этой книгой Вы познакомитесь с основными терминами и понятиями ИИ, такими как машинное обучение, глубокое обучение и нейронные сети. Научитесь создавать...
Springer, 2023. — 235 р. — ISBN 978-3-031-18551-9. The Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modelling, and Applications presents the state of the art of the application of Machine Learning (ML) and Deep Learning (DL) in the domain of finance. We will present a combination of empirical evidence to diverse fields of finance so...
Packt Publishing, 2015. — 190 p. — ISBN: 978-1-78439-908-5. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine...
Leanpub, 2020. — 274 p. — ISBN: 978-1999579579. From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI...
Springer, 2018. — 317 p. — ISBN: 978-3319730394. This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General...
3rd edition. — Springer, 2021. — 458 p. — ISBN 978-3-030-81934-7. This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more...
Springer, 2020. — 591 p. — (Springer Series in the Data Sciences). — ISBN: 978-3-030-39567-4ю This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the...
O’Reilly, 2017. — 388 p. — ISBN: 978-1-491-91721-3. Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the...
3rd ed. — Springer, 2023. — 974 p. — ISBN 3031246276. This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook . Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are...
Springer, 2019. — 155 p. — ISBN: 981133028X This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis....
Packt Publising, 2015. — 405 p. — ISBN13: 978-1783982042. About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset from the churn dataset,applying different classification methods. Who This Book Is For If you want to learn how to use R for machine...
М.: Альпина ПРО, 2021. — 335 с.— ISBN 978-5-907394-29-2 Мы живем во время революции, еще 50 лет назад казавшейся невероятной, — революции в области умных машин, которые теперь обучаются самостоятельно, вместо того чтобы просто выполнять запрограммированные команды. И возможности таких машин огромны: распознавание изображений, лиц и голосов, переводы на сотни языков, беспилотное...
Wiley, 2015. — 360 p. — ISBN: 978-1-118-96174-2. Целевая аудитория: опытные разработчики на Python. В настоящее время машинное обучение является одной из самых активных и быстро развивающихся областей науки. Результатом такой активности ML становится появление многочисленных библиотек и фреймворков языка Python для работы с данными, статистикой, математикой, биологией и многим...
Independently published, 2018. — 79 р. Do you want to impress the processes that you are working on? Do you want to make your machines more intelligent? If your answer to any of those questions is yes, then you have come to the right place. This book is a sequel to the book titled 'Machine Learning: A Step-by-Step guide.' In the first book, you gathered information on what...
Mercury Learning & Information, 2020. — 363 p. — ISBN: 978-1-68392-495-1. This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is...
Independently published, 2021. — 225 p. — ASIN B09BNYDH5F. Have you always been curious about machine learning but do not know where to start. Or perhaps your new job requires you to learn machine learning but you are overwhelmed with all the information online. What is machine learning? What is Scikit-Learn? What does the fit() method that you see on so many online tutorials...
New York: W. W. Norton & Company, 2020. — 510 p. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us?and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end,...
CRC, 2020. — 430 p. — ISBN: 9781466510845. Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the...
Amazon.com Services LLC, 2020. — 120 p. — ISBN: 979-8615125096. You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you’ve always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and...
World Scientific Publishing Company, 2020. — 550 p. — ISBN: 978-9811207716. This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory...
Addison-Wesley Professional, 2018. — 272 p. — ISBN: 9780134863924, 0134863860 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you...
Springer, 2020. — 261 p. — ISBN: 3030266214. This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of...
Springer, 2022. — 233 p. — (Machine Learning: Foundations, Methodologies, and Applications). — ISBN 9811681929. Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the...
Packt, 2019. - 458p. - 978-1788295864 3rd.ed. Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by...
Wiley, 2021. — 216 p. — ISBN 978111968236. Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series...
Wiley-IEEE, 2020. — 474 p. — ISBN: 9781119562252. Due to its powerful nonlinear mapping and distribution processing capability, deep NN-based machine learning technology is being considered as a very promising tool to attack the big challenge in wireless communications and networks imposed by the explosively increasing demands in terms of capacity, coverage, latency, efficiency...
O’Reilly, 2017. — 392 p. — ISBN: 978-1-4493-6941-5. Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data...
Wiley, 2020. — 464 p. — ISBN: 1119591511. Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning―a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions―allows organizations to...
O’Reilly Media, 2018. — 209 p. — ISBN10: 149199584X, 13 978-1491995846. EPUB (True/HQ) Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook,...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Cambridge Scholars Publishing, 2022. — 365 p. — ISBN 978-1-5275-8324-5. This book is a collection of real-world cases, illustrating how to handle challenging and volatile financial time series data for a better understanding of their past behavior and robust forecasting of their future movement. It demonstrates how the concepts and techniques of statistical, econometric,...
Wiley, 2023. — 701 р. — ISBN 978-1119828792. Machine Learning — also known as data mining or data analytics — is a fundamental part of Data Science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive...
O’Reilly Media, 2020. — 432 p. — ISBN: 978-1-492-07305-5. Code files only! Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in...
Springer, 2019. — 552 p. — (Learning and Analytics in Intelligent Systems 01). — ISBN: 978-3-030-15627-5. This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies....
3rd Edition. — Springer, 2020. — 451 p. — (Springer Texts in Statistics). — ISBN: 978-3-030-40188-7. This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern...
Springer, 2021. - 108p. - ISBN 9783030683788 This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in...
Machine Learning Mastery, 2018. — 115 р. Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own...
Mercury Learning and Information, 2024. — 169 p. This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code...
New York: Morgan & Claypool, 2017. — 128 p. Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the...
Amazon Digital Services LLC, 2016. — 63 p. Artificial intelligence is changing our lives in ways we need to understand. Algorithms govern how we find information, how we learn, how we move, how we buy, what we buy, how we stay healthy, how we meet, whom we meet, how we are treated and what we are treated with. Marketing, analytics, diagnostics, manufacturing, driving,...
Morgan Kaufmann, 2014. — 323 p. — ISBN: 0123985374, 9780123985378 The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction....
Cambridge: The MIT Press, 2001. — 568 p. This is the first textbook that provides a thorough, comprehensive and unified introduction to the field of learning from experimental data and soft computing. Support vector machines (SVMs) and neural networks (NNs) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed...
Wiley, 2019. — 236 p. — ISBN10: 1119548217, 13 978-1119548218. Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial...
Apress Media LLC., 2020. — 570 p. — ISBN13: 978-1-4842-5173-7. Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic...
Morgan & Claypool, 2022. — 110 p. — (Synthesis Lectures on Artificial Intelligence and Machine Learning). — ISBN 3031791665. Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time,...
Packt, 2022. — 440 p. — ISBN 1801072167, 9781801072168. Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building,...
O’Reilly Media, 2022. — 300 p. — ISBN-13: 978-1-098-10682-9. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you’re looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you....
O’Reilly Media, 2023. — 271 p. — ISBN-13: 978-1-098-11722-1. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use...
Springer, 2021. — 655 p. — ISBN 9783030625818, EISBN 9783030625825. This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging...
Apress, 2019. — 637 p. — ISBN: 1484253159. Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data...
CRC Press, 2020. — 366 p. — ISBN13: 978-1-138-60182-6. This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of...
Учебное пособие. – СПб: Университет ИТМО, 2022. – 53 с. В настоящем учебном пособии рассмотрены методы машинного обучения, уже ставшие классикой: кластеризация, ассоциативные правила, обучение с учителем, ансамблевые методы. Учебное пособие предназначено для освоения соответствующих разделов дисциплин «Архитектура систем ИИ», «Инструментальные средства для систем ИИ»,...
2nd Edition. — Springer, 2022. — 582 p. — ISBN 978-3-030-96622-5. This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book...
CRC Press, 2021. — 339 p. — ISBN: 978-0367336745. The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book...
CRC Press, 2023. — 310 p. This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on...
Butterworth-Heinemann, 2018. — 364 p. — ISBN: 978-0-12-812976-0. This book presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those...
Монография 2018. - 442 с. На английском языке. Эта книга реализует много общих алгоритмов машинного обучения в эквивалентных R и Python. Это 3-е издание книги. В книге рассматриваются реализации R и Python различных регрессионных моделей, алгоритмы классификации, в том числе логистическая регрессия, классификация KNN, SVM, b-сплайны, случайный лес, форсирование и т. д. Далее в...
Boca Raton: CRC Press, 2024. — 671 p. This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. In recent decades, machines have played a significant role in making human life more comfortable. Machine...
Б.м.: Samuel Hack, 2020. — 168 p. Your Free Gift Machine Learnini for Beginners The Purpose of This Book What is Artificial Intelligence? How is machine learning used? Recent Advancements in Data Analysis Introduction to Statistics Choosing the right kind of model for machine learning Supervised learning Classifications Unsupervised Learning Neural networks Reinforcement...
O’Reilly, 2019. — 320 p. — ISBN: 1492047546. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for...
New York: amazon.com Services LLC, 2020. — 37 p. Are you prepared for the inevitable AI revolution? How can you use it in your current role as a business leader? Artificial intelligence has become the center of strategic decision making for organizations. It disrupts the way industries function - from sales and marketing to finance and HR, companies are betting on AI to give...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN 978-1-492-08525-6. • 2021-12-07: First Release Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning...
Wiley, 2018. — 268 p. — (Wiley series in probability and statistics). — ISBN: 978-1-119-43898-4. Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in...
Springer, 2015. — 296 p. — ISBN: 9783319200095. This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers,...
Packt, 2019. - 298p. - ISBN: 1789612853 Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in...
No Starch Press, 2021. — 273 p. — ISBN 9781718500563. A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence....
Oxford University Press, 2021. — 333 p. — ISBN 9780190941666. Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data science that requires only a few weeks to learn instead of years of training Though the concept of computers learning to solve a problem may still conjure thoughts of futuristic...
3rd edition. — Packt Publishing, 2020. — 527 p. — ISBN 9781800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data scientists...
Artech House, 2021. — 349 p. — ISBN 978-1-63081-775-6. This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation,...
Cham: Springer International Publishing, 2019. — 206 p. — ISBN: 978-3-319-99223-5. This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational...
Apress, 2021. — 127 p. — ISBN 978-1912807130. Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an...
Manning Publications, 2020. — 513 p. — ISBN: 978-1617296574. Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Code files only! Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a...
Packt, 2019. - 664p. - ISBN: 9781838827878 Your one-stop guide to working with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js A handy guide to make your web applications smart from scratch using example-based approach Use flexible and...
Apress Media LLC., 2020. — 392 p. — ISBN13: 978-1-4842-6155-2. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and...
De Gruyter, 2018. — 183 p. — (Frontiers In Computational Intelligence). — ISBN: 978-3110550320. This volume comprises six well-versed contributed chapters devoted to report the latest findings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is...
Albert Bifet, Ricard Gavalda, Geoff Holmes, Bernhard Pfahringer. — MIT Press, 2018. — 288 p. — ISBN: 978-0-262-03779-2. Machine Learning for Data Streams: with Practical Examples in MOA (Adaptive Computation and Machine Learning series) A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available...
New York: Apress, 2022. — 290 p. Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use....
Independently published, 2020. — 210 p. — ISBN: 979-8611346952. As a beginner you might think that programming is complex... Learning artificial intelligence coding can take months, and the possibility to give up before mastering it could be high. So, if you have a project to develop you could think on hiring a professional developer to shorten the time. This may seem like a...
Microsoft Press, 2020. — 392 p. — ISBN 978-0-13-556566-7. Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps...
New York: Addison-Wesley Professional, 2019. — 592 p. The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyonewill help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is...
CRC Press, 2023. — 478 p. — ISBN 978-1-032-26829-3. Машинное обучение: концепции, методы и приложения Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining Machine Learning and goes on to explain the basis of Machine Learning algorithms. The mathematical foundations required are outlined along with their associations to Machine...
Routledge, 2023. — 191 p. — ISBN: 978-1-032-07277-7. Машинное обучение для бизнес-аналитики: анализ данных в реальном времени для принятия решений Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data...
Birmingham: Packt Publishing, 2019. — 482 p. — ISBN: 1789952298. Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data ! Key Features Learn how to select the most suitable Python library to solve your problem. Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them. Delve into...
Springer, 2018. - 525p. - ISBN: 9811312796 This book presents high-quality papers from an international forum for research on computational approaches to learning. It includes current research and findings from various research labs, universities and institutions that may lead to development of marketable products. It also provides solid support for these findings in the form...
CRC Press, 2022. — 298 p. — ISBN 0367433540. Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and...
Издательство CRC Press, 2012, -316 p. "Cost-Sensitive Machine Learning" is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine...
New York: Springer, 2020. — 202 p. This book discusses one of the major applications of Artificial Iintelligence (AI): the use of Machine Learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also...
Apress, 2020. — 144 p. — ISBN: 1484259394. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices...
Artech House, 2025. — 265 p. — ISBN-13: 978-1-68569-033-5. Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification...
Manning Publications, 2023. — 362 p. — (Final Release). — ISBN 9781617299520. Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Summary In Evolutionary Deep Learning you will learn...
Cambridge University Press, 2018. — 162 p. — ISBN: 978-1-107-18458-9. This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced...
2nd Edition. — Leanpub, 2022-03-04. — 329 р. This book teaches you how to make Machine Learning models more interpretable. Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of Machine Learning. This book is about making Machine Learning models and their...
O’Reilly Media, Inc., 2023. — 279 p. — ISBN 978-1-098-11913-3. Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for...
Springer, 2014. — 120 p. As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to...
Wiley, 2022. — 464 p. — (Wiley Finance series). — ISBN 9781119791386 State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The...
Independently published, 2023. — 258 p. "Mathematics for Machine Learning: A Deep Dive into Algorithms" is a comprehensive guide that bridges the gap between mathematical theory and practical applications in the dynamic world of Machine Learning. Whether you're a data science enthusiast, a budding Machine Learning engineer, or a seasoned practitioner, this book equips you with...
Packt Publishing, 2018. — 240 р. — ISBN: 978-1789803556. As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate...
CRC Press, 2023. — 356 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN 9780429292835. Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering...
Singapore: World Scientific Publishing Company, 2021. - 881 p. - ISBN 9811241198. This unique compendium gives an updated presentation of clustering, one of the most challenging tasks in machine learning . Clustering is a part of machine learning that seeks to identify groups into sets of objects such that objects that belong to the same group are as similar as possible , and...
NY: InfoQ, 2018. — 40 p. Machine learning (ML) and deep-learning technologies like Apache Spark, Flink, Microsoft CNTK, TensorFlow, and Caffe brought data analytics to the developer community. Whether it's classifying 2 million sales products received from over 700 multinational retailers for the "Love the Sales" website, building awareness of hindsight bias with customers at...
2nd Edition. — CRC Press, 2023. — 528 p. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory....
O’Reilly Media, Inc., 2024. — 359 p. — ISBN 978-1-098-15161-4. What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that...
Reactive Publ., 2023. — 663 p. Unlock the full potential of machine learning with "Machine Learning," your essential guide to conquering the cutting-edge tools that are reshaping the landscape of Data Science and Artificial Intelligence (AI). Whether you're a student, a Data Science professional, or simply a tech enthusiast eager to delve into the world of Machine Learning,...
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
Пер. с англ. С. Черников. — СПб.: Питер, 2024. — 512 с.: ил. — (Для профессионалов). — ISBN: 978-5-4461-1788-8. Фундаментальные математические дисциплины, необходимые для понимания машинного обучения, — это линейная алгебра, аналитическая геометрия, векторный анализ, оптимизация, теория вероятностей и статистика. Традиционно все эти темы размазаны по различным курсам, поэтому...
O’Reilly Media, 2018. — 216 p. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem,...
Khaled Bayoudh, 2017. — 166 p. There are a growing number of people who are seeking to understand the main concepts of Machine/Deep Learning and what powers them up. And if you are of these people, then this book is for you! This book discusses the Machine/Deep Learning algorithms, methods, concepts, functions and code that make Deep Neural Networks such as Convolutional Neural...
O’Reilly Media, 2016. — 82 p. — ISBN: 978-1-491-96297-8. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding...
Cambridge University Press, 2009. — 716 p. — ISBN 978-0-521-83378-3. This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can be solved...
2nd Edition. — Springer, 2015. — 561 p. — ISBN: 9781447168409. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it...
Amazon.com Services LLC., 2020. — 140 p. Are you looking for a guide that will teach you all you need to know about machine learning? Are you looking for a way to learn how to write algorithms from scratch? Then read on… Artificial intelligence is a common part of our lives, and we use it daily. Machine learning is one application of artificial intelligence and is where...
Manning Publications, 2020. — 220 p. — ISBN: 978-1617294884. How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore...
Packt Publishing, 2018. — 254 р. — ISBN: 978-1789806199. Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book DescriptionMachine...
New York: Springer, 2022. — 179 p. This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key...
Springer, 2021. — 328 p. — ISBN 978-3-030-83355-8. Объяснимый искусственный интеллект: введение в интерпретируемое машинное обучение This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and...
CreateSpace Independent Publishing Platform, 2018. — 210 p. — ISBN: 978-1986406857 (paperback). Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for...
CRC Press, 2022. — 243 p. — (Computational Intelligence in Engineering Problem Solving). — ISBN 978-1-032-04723-2. Машинное обучение и Интернет вещей для интеллектуальных систем и интеллектуальных приложений The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being...
Cambridge University Press, 2022 — 337 p. — ISBN: 1316518906. Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that...
Springer, 2021. — 252 p. — ISBN 978-3-030-70387-5. All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines―mechanical, aerospace,...
Amazon Digital Services LLC, 2018. — 50 р. Excited about computer science, machine learning and programming? This book is the ultimate guide to Python programming for beginners and intermediates! Considering that Python is used by big tech companies like Google, Nokia, IBM, Yahoo!, Pinterest, Disney and many others, it makes a lot of sense to prioritize learning Python if you...
Packt Publishing, 2019. — 342 р. — ISBN: 978-1788996921. This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques....
No Starch Press, 2024. — 264 р. — ISBN-13: 978-1-7185-0377-9. Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and...
Manning Publications, 2020. — 536 p. — ISBN: 978-1617296574. Code files only! Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner....
Morgan Kaufmann, 2019. — 327 p. — ISBN: 978-0-12-814623-1. This book presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general...
O’Reilly Media, 2023. — 450 р. — ISBN 978-1-098-12020-7. Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can...
IGI Global, 2020. - 700p. - ISBN 1522596437 As todays world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give...
Boca Raton: CRC Press, 2022. — 477 p. — ISBN-13 9781003258803. This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for...
Springer, 2021. — 260 p. — ISBN 978-981-15-7876-2. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a...
Oxford University Press, 2020. — 257 p. — ISBN: 978019256309-5. Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and...
Packt, 2019. - 346p. - ISBN: 9781789614671 Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and...
Wiley, 2022. — 585 p. — ISBN 9781119716747. Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can...
Packt Publishing, 2023. — 270 p. Key Features Design, build, and run microservices systems that utilize the full potential of machine learning Discover the latest models and techniques for combining microservices and machine learning to create scalable systems Implement machine learning in microservices architecture using open source applications with pros and cons Book...
The MIT Press, 2024. - 394 p. - ISBN 0262049376. The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches. Multi-Agent Reinforcement Learning (MARL) , an area of machine learning in which a collective of agents learn to optimally interact in a...
Independently published, 2021. — 171 p. — ISBN 979-8724724272. Machine Learning is rapidly changing the world and it is the way of the Future of Human Technology. Art, information, processes, calculations, emotions will be rapidly learned and discover from machines. Do you want to learn more about the world of Machine Learning and its Applications? Would you like to improve and...
Springer, 2018 - 257 p. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based...
Packt Publishing, 2017. — 382 p. — ISBN: 978-1-78398-028-4. Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of...
BPB Publications, 2020 — 262 p. — ISBN 9389845424 , 978-9389845426. Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature...
New York: CRC Press/Taylor & Francis Group, 2019. — 345 p. The book focuses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including...
Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad (Editor). — Wiley, 2020. — 535 p. — ISBN: 9781119654742. Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online...
Microsoft Press, 2020. — 450 p. — ISBN: 978-0-13-556566-7. Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic...
World Scientific Publishing, 2021. — 896 p. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout Machine Learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming,...
Packt Publishing - ebooks Account, 2016. — 614 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689 This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working...
Birmingham: Packt Publishing, 2018. — 263 p. — ISBN: 1788629353. Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease ! Key Features Build smart mobile applications for Android and iOS devices. Use popular machine learning toolkits such as Core ML and TensorFlow Lite. Explore cloud services for machine learning that can be used...
New York: Springer, 2020. — 243 p. This book discusses various Machine Learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together Machine Learning, cognitive science and other aspects of Artificial Intelligence (AI) to help provide a roadmap for future research on intelligent systems, the book is a valuable reference...
London, United Kingdom: Institution of Engineering and Technology, 2019. — 350 p. — ISBN: 978-1785616570, 1785616579. Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of...
Springer, 2021. — 214 p. — (Lecture Notes in Mathematics 2293). — ISBN 978-3-030-77561-2. от геометрии к физике и машинному обучению The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to...
Packt Publishing, 2016. — 298 p. — ISBN: 978-1-78588-660-7. Explore the web and make smarter predictions using Python Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps...
New York: Apress, 2018. — 355 p. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine...
BPB Publishing, 2021 — 751 p. — ISBN 9789391030421. Master the ML process, from pipeline development to model deployment in production. 'Practical Full-Stack Machine Learning' introduces data professionals to a set of powerful, open-source tools and concepts required to build a complete data science project. This book is written in Python, and the ML solutions are...
Packt Publishing, 2018. — 2014 p. — ISBN: 1789138132. !Code files only START READING Book Description Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them...
Springer, 2023. — 302 p. — ISBN 978-3-031-35050-4. This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of...
Boca Raton: CRC Press, 2025. — 415 p. Mathematical Engineering of Deep Learning provides a complete and concise overview of Deep Learning using the language of mathematics. The book provides a self-contained background on Machine Learning and optimization algorithms and progresses through the key ideas of Deep Learning. These ideas and architectures include deep neural...
Morgan & Claypool Publishers, 2019. — 160 p. — ISBN: 9781681734408, 9781681734415, 9781681734422. The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often,...
Apress Media LLC, 2024. — 342 p. — ISBN-13 979-8-8688-0375-8. MLOps с Ray: лучшие практики и стратегии внедрения операций машинного обучения Understand how to use MLOps as an engineering discipline to help with the challenges of bringing Machine Learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate Machine...
NY: InfoQ, 2017. — 36 p. Machine learning has long powered many products we interact with daily—from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances in...
CRC Press, 2022. — 447 p. — ISBN 978-0-367-67989-7. Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models...
Publisher: Chapman and Hall/CRC; April, 2009, 457 pages. A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Linear discriminants The multi-layer perceptron Radial basis functions and splines Support vector machines Learning with trees Decision by committee: ensemble learning Probability and learning Unsupervised learning Dimensionality reduction...
Independently published, 2023. — 72 p. — ASIN: B0BRW8J2FG. Machine Learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that leverage large amounts of data and advanced modeling techniques. In this book, we provide a comprehensive overview of Machine Learning for quantitative trading, covering...
De Gruyter, 2023. — 478 р. — ISBN 978-3-11-078597-5. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
Stylianos Papadakis, Alexandros Garefalakis, Christiana Chimonaki, Christos Lemonakis, Constantin Zopounidis — IGI Global, 2021. — 330 p. — (Advances in Finance, Accounting, and Economics (AFAE)).— ISBN 1799848051, 9781799848059. The prediction of the valuation of the quality of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in...
O’Reilly Media, 2019. — 359 p. — ISBN: 9781492035640. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to...
John Wiley & Sons, 2012. — 404 p. — ISBN: 978-0470596692. Learn the science of collecting information to make effective decisions. Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed...
Apress, 2017. - 358p. - ISBN: 978-1484228654 Code files only! Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning...
O’Reilly Media, 2020. — 432 p. — ISBN: 978-1-492-07305-5. Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised,...
Wiley, 2024. — 510 p. — ISBN 9781394220632. Теория и приложения машинного обучения: практические примеры использования Python на классических и квантовых машинах Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications delves into the...
Johns Hopkins University Press, 2024. — 280 р. —ISBN: 978-1-4214-4923-4. How AI is revolutionizing the future of learning and how educators can adapt to this new era of human thinking. Artificial Intelligence (AI) is revolutionizing the way we learn, work, and think. Its integration into classrooms and workplaces is already underway, impacting and challenging ideas about...
Cambridge: Cambridge University Press, 2023. — 468 p. — ISBN 1009098381. The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with...
Springer, 2021. — 460 p. — ISBN 9811519668. Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced...
Пер. с англ. канд. хим. наук А.Г. Глузикевича, под ред. В.Р. Гинзбурга. — СПб..: Диалектика, 2020. — 432 с.: ил. — ISBN 978-5-907144-99-6. По мнению многих отраслевых экспертов, обучение без учителя — передовой рубеж технологий искусственного интеллекта (ИИ) и, возможно, ключ к созданию сильного ИИ. Поскольку подавляющая часть накопленных в мире данных не размечена, к ним...
Астана: АЛИСТ, 2025. — 296 с. — ISBN 978-601-08-4725-5. В книге подробно представлены три проблемно-ориентированных вида машинного обучения (Machine learning, ML): автоматизированное обучение AutoML без кодирования, обучение BigQuery ML с малым объемом кодирования и обучение с применением пользовательского кода на основе библиотек scikit-learn и Keras. При этом от читателя не...
Springer, 2023. — 327 p. Hyperparameter tuning? Is this relevant in practice? Is it not rather an academic gimmick? This book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of Machine Learning (ML) and Deep Learning (DL) methods. Programming code is provided so that...
O’Reilly Media, 2022. — 334 p. — ISBN-13 978-1-492-08992-6. Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train...
Bentham Science Publishers, 2022. — 360 p. — ISBN 978-1-68108-940-9. Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include...
Leanpub, 2018. — 147 p. Do you want to start using R for crunching machine learning models right from the start with examples? The book is about quickly entering the world of creating machine learning models in R . The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules. The...
Independently published, 2020. — 201 p. — ISBN: 979-8611356791. Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? Or perhaps you want to know how to analyze data to solve simple domestic issues but you don’t know how to do it? Here’s the deal… As a beginner you will be probably afraid that...
2nd Edition. — O’Reilly Media, Inc., 2024. — 556 p. — ISBN 978-1-098-15601-5. Производственные системы машинного обучения: инженерные модели и конвейеры машинного обучения. Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from...
Dargslan Publishing, 2024. — 607 p. "Machine Learning with Python: A Comprehensive Guide" is your gateway to mastering the exciting world of Machine Learning (ML) using Python. This book is meticulously crafted to cater to beginners, professionals, and students alike, offering a step-by-step approach to understanding and implementing ML concepts. Key Features: Accessible...
New York: Nova Science Publishers, Inc., 2021. — 810 p. — (Computer Science, Technology and Applications). — ISBN 978-1-53619-813-3. With the emergence of revolutionary technological standards such as 5G and Industry 4.0, real time applications which require both cloud computing and machine learning are becoming increasingly common. Examples of such applications include...
Leanpub, 2022-08-22. — 155 р. Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable Machine Learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods. Feature...
O’Reilly, 2017. — 581 p. — ISBN: 9781491962299. Only sample files! Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete...
Morgan Kaufmann/Elsevier, 2024. — 410 p. — ISBN-13: 978-0-443-21857-6. Synthetic Data and Generative AI covers the foundations of Machine Learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression...
Walter de Gruyter GmbH, 2021. — 216 p. — ISBN 978-3-11-070248-4. Machine learning (ML) is a part of computerized reasoning which comprises algorithms and artificial neural networks and displays qualities firmly connected with human insight. The book focuses on the applications of ML for sustainable development. This book provides an understanding of sustainable development and...
Springer, 2024. - 464 p. - ISBN 3031469895. Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals . The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory...
Academic Press, 2021. — 661 p. — ISBN 9780323901994. Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of...
Toronto: John C. Hull, 2020. — 274 p. Unsupervised Learning Linear and Logistic Regression Decision Trees SVMs Neural Networks Reinforcement Learning Natural Language Processing Model Interpretability Applications in Finance Issues for Society Answers to End of Chapter Questions Glossary of Terms
Boston: Kluwer Academic Publishers, 2004. - 220 p. Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of...
Packt, 2018. - 356p. - ISBN: 9781788998246 A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning...
Springer, 2016. — 120 p. — (Studies in Big Data 20). — ISBN: 9783319333816, 9783319333830 This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction...
New York: Springer, 2021. — 320 p. This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics. Accordingly, there is a pressing need for...
O’Reilly Media, Inc., 2020. — 408 p. — ISBN: 978-1-098-11578-4. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts...
Apress, 2019. — 384 p. Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance,...
No Starch Press, 2024. — 272 p. — ISBN-13: 978-1718502109. Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the...
New York: amazon.com Services LLC, 2020. — 92 p. Interested in the field of Artificial Intelligent (AI)? Then this book is for you! This book has been designed to learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of AI. With every tutorial you will develop new skills and improve your understanding of this...
Ellis Horwood Publishing. 1994. - 298 p. The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. Before describing the contents, we first need to define what we mean by classification, give some...
Leanpub, 2022. — 112 p. Build a career while doing technical wriitng. I earned $300 for my first paid Data Science and Machine Learning article. I get paid between $250 and $500 for each Data Science article I write. In this ebook, I'll show you how you too, can earn while writing about Data Science and Machine Learning. You have been learning about Data Science and Machine...
Apress, 2022. — 343 p. — ISBN 9781484278017, 9781484278024. Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms. You’ll begin by reviewing the theoretical aspects of machine learning...
Manning, 2023. — 219 p. Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn...
Stanford University, 1998. — 179 p. My intention is to pursue a middle ground between a theoretical textbook and one that focusses on applications. The book concentrates on the important ideas in machine learning. I do not give proofs of many of the theorems that I state, but I do give plausibility arguments and citations to formal proofs. And, I do not treat many matters that...
O’Reilly Media, 2021. — 304 p. — ISBN 1492053279, 9781492053279. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads–a process Kubeflow makes much easier. This practical book shows data scientists, data...
3rd edition. — Packt Publishing, 2019. — 771 p. — ISBN 9781789955750. Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of...
New York: Springer, 2022. — 362 p. This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of Machine Learning communities across the world in recent years....
New York: amazon.com Services LLC, 2021. — 403 p. Machine learning can be broadly classified into supervised and unsupervised learning. By definition, the term supervised means that the “machine” (the system) learns with the help of something—typically a labeled training data. Training data (or a dataset ) is the basis on which the system learns to infer. An example of this...
Cambridge University Press, 2023. — 412 p. — ISBN 978-1-009-12330-3. Parked with real-world examples, industry insights, and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are...
Springer, 2019. — 137 p. — ISBN: 3030170756. This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part , the authors propose algorithms to find local minima in nonconvex...
Manning Publications, 2023. — 550 p. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including. Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for...
Интернет-издание, 2019. — 319 с. В курсе изучаются классические и продвинутые алгоритмы машинного обучения, подробно разбираются математические обоснования изучаемых методов. После прохождения курса вы сможете полностью решать классические задачи анализа данных: начиная от сбора и хранения данных и заканчивая внедрением обученных моделей в продакшн. Курс предназначен для тех,...
O’Reilly Media, 2023. - 325 p. - ISBN 1098146824. Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts . This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras . In each case, you'll learn key ML...
Leanpub, 2020. — 295 p.— ISBN B084FXKCS8. This version was published on 2020-07-13 This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine...
Independently Published, 2019. — 151 p. — ASIN B082MPL2Z2. One of the most widely recognized AI methods utilized for handling huge information is AI, a self-versatile calculation that shows signs of improvement examination and examples with experience or with recently included information. In the event that a computerized installment organization needed to identify the event or...
Springer, 2018. — 493 p. — ISBN: 978-3319735306. Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1...
Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, Nisheeth Joshi. — John Wiley & Sons, Inc... 2022. — 271 p. — ISBN 978-1119775614. Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML)...
Springer London, 2013. — 388 p. — ISBN 978-1-4471-5184-5, ISBN 978-1-4471-5185-2. This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such...
Springer, 2019. — 418 p. — ISBN: 3030261522. The book introduces the latest methods and algorithms developed in machine and deep learning (hybrid symbolic-numeric computations, robust statistical techniques for clustering and eliminating data as well as convolutional neural networks ) dealing not only with images and the use of computers, but also their applications to...
Apress, 2018. - 362p. - ISBN: 1484235630 Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a...
IGI Global, 2020. — 328 p. — ISBN: 978-1799803010. In todays developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the...
Apress, 2019. — 354 p. — ISBN: 978-1484251201. Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Packt Publishing, 2018. — 576 p. — ISBN: 978-1788621113. !Code files only Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms...
Leanpub, 2021. — 374 p. Learn how to leverage the power of machine learning and deep learning to analyze behavioral patterns from sensors data and electronic records. This book shows you how to explore, preprocess, encode, and visualize your data This is the first version (Leanpub) of the book. The most recent version is available for free in HTML at...
Scrivener Publishing LLC., Wiley, 2022. — 470 p. — (Advances in Learning Analytics for Intelligent Cloud-IoT Systems). — ISBN 978-1-119-76225-6. Методы машинного обучения и аналитика для облачной безопасности This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine...
Wiley, STE Ltd., 2022. — 255 p. — (Computer Science, Operational Research and Decision). — ISBN 978-1-78945-071-2. Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the...
Cuantum Technologies, 2024. — 614 p. Become a Machine Learning Hero and Master Data Science with Python. In a world driven by data, mastering Machine Learning is your key to unlocking new opportunities and solving complex problems. Whether you're a beginner or a professional looking to sharpen your skills, Machine Learning Hero: Master Data Science with Python Essentials is...
New York: Springer, 2022. — 202 p. This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial...
Independently published, 2024. — 238 p. ISBN-13 979-8332452369. This book delves into the intricate relationship between mathematics and Machine Learning, providing readers with a comprehensive understanding of the mathematical concepts that underpin modern AI. From linear algebra and calculus to probability theory and statistics, each chapter explores a different mathematical...
Springer, 2010. — 474 p. — ISBN: 978-3642141249, e-ISBN: 978-3642141256. The topic of preferences is a new branch of machine learning and data mining , and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals....
London: Academic Press, 2022. — 398 p. Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better...
O’Reilly Media, 2023. — 466 p. — ISBN 1098102436. The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true...
O’Reilly, 2019. — 320 p. — ISBN: 1492047546. !Code files only With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project....
Leanpub, 2020. — 505 p. — ISBN NA. This version was published on 2020-10-09 Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11. Important: I will not be updating this book to the new features introduced with iOS 15 and...
Packt, 2018. — 503 p. — ISBN: 178934641X The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to...
Packt, 2018. — 503 p. — ISBN: 178934641X. Code files only! The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book...
Packt Publishing, 2019. — 294 р. — ISBN: 978-1788830577. Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right...
3rd ed. — Packt, 2019. — 458 p. — ISBN: 1788295862. Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by...
CRC Press, Taylor & Francis Group, LLC., 2022. — 404 p. — ISBN 9780367550547. Пространственное прогнозное моделирование с помощью R Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based...
Benjamin M. Marlin, 2008. A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy. Decision Theory, Inference, and Learning Optimal Prediction and Minimizing Expected Loss The Bayesian Framework The Maximum a Posteriori Framework The Direct Function Approximation Framework Empirical Evaluation Procedures A Theory of Missing Data Categories...
Packt Publishing, 2021. — 737 p. — ASIN B08PDFXXRL. Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with...
Springer Cham, 2024. — 249 p. — eBook ISBN 978-3-031-56431-4. Clearly explains the mathematical underpinnings essential for a robust understanding of machine learning algorithms Coverage is tailored to students and researchers in all natural science areas, in addition to computer scientists Accessible yet rigorous mathematical approach supplemented with website and instructor...
O’Reilly, 2019. - 362p. - ISBN: 1492035645 !Code files only Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be...
Wiley, 2012. — 416 p. — (Wiley Series in Probability and Statistics). — ISBN: 0470596694, 9780470596692. Learn the science of collecting information to make effective decisionsEveryday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information...
CRC Press, 2022. — 211 p. — ISBN 978-1-032-07401-6. Physics of Data Science and Machine Learning links fundamental concepts of physics to Data Science, Machine Learning and Artificial Intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data...
Springer, 2019. — 320 p. The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many constraints: (pseudo)...
Independently published, 2021. — 269 p. — ISBN B089GQM3QD. Machine learning is a computer programming technique in which software is built in such a way that it can learn new facts from itself and make decisions on its own when necessary. Machine learning (ML) is a large discipline, and this book covers a lot of ground. We attempted to cover all aspects of the subject. This...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
Manning Publications, 2018. — 224 p. — ISBN: 978-1-6172-9333-7. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
Packt, 2019. - 162p. - ISBN: 9781838825669 Teach your machine to think for itself! Key Features Delve into supervised learning and grasp how a machine learns from data Implement popular machine learning algorithms from scratch, developing a deep understanding along the way Explore some of the most popular scientific and mathematical libraries in the Python language Book...
IGI Global, 2024. — 315 p. In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of...
O’Reilly Media, Inc., 2024. — 402 p. — ISBN: 978-1-098-14463-0. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung,...
Packt Publishing, 2018. — 566 p. — ISBN: 1788390040. Practical, hands-on solutions in Python to overcome any problem in Machine Learning Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to...
2nd Edition. — Academic Press, 2020. — 1160 p. — ISBN 9780128188033. Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods,...
De Gruyter, 2024. — 210 р. — ISBN: 978-3-11-128899-4. This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory...
New York: Springer, 2020. — 256 p. This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities,...
Издательство Idea Group, 2007, -384 p. Machine learning is the study of how to build computer programs that improve their performance at some task through experience. The hallmark of machine learning is that it results in an improved ability to make better decisions. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not...
New York: Springer, 2018. — 485 p. With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for...
СПб.: Питер, 2019. — 432 с.: ил. — (IT для бизнеса). — ISBN 978-5-4461-1154-1. Маркос Лопез де Прадо делится тем, что обычно скрывают — самыми прибыльными алгоритмами машинного обучения, которые он использовал на протяжении двух десятилетий, чтобы управлять большими пулами средств самых требовательных инвесторов. Машинное обучение меняет практически каждый аспект нашей жизни,...
Учебно-методическое пособие. — Екатеринбург: Уральский университет, 2023. — 124 с. Учебно-методическое пособие посвящено изучению базовых методик машинного обучения для решения задач регрессии, классификации и кластеризации. Основной акцент делается на приобретении навыков решения практических задач различными методами, поэтому пособие содержит большое количество примеров, для...
Springer, 2020. - 121p. - ISBN: 9813340215 This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly...
Wiley, 2023. — 592 p. — eBook ISBN: 978-1-394-20910-1. In-depth resource covering machine and deep learning methods using MatLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MatLAB introduces early career professionals to the power of MatLAB to explore machine and deep learning applications by explaining the...
Cambridge: Cambridge University Press, 2025. — 1158 p. — ISBN 1009170244. Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then...
Springer, 2021. — 182 p. — (SpringerBriefs in Petroleum Geoscience & Engineering). — ISBN 978-3-030-71767-4. This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used...
Apress, 2019. — 248 p. — ISBN13: 978-1-4842-5106-5. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective...
CRC Press, 2020. — 280 p. — ISBN13: 978-1-138-32339-1. Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both...
CRC Press, 2024. — 306 p. This book addresses the growing need for Machine Learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, Machine Learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working Machine...
New York: Springer, 2007. — 279 p. — ISBN: 978-0-387-69938-7, e-ISBN: 978-0-387- 69942-4. The objectives we set for this book are two-fold: (1) bring together those important machine learning techniques that are particularly powerful and effective for modeling multimedia data; and (2) showcase their applications to common tasks of multimedia content analysis. Multimedia data,...
Springer, 2021. — 293 p. — ISBN 978-3030712693. This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select...
Springer, 2020. — 353 p. — ISBN: 978-3-030-49394-3. This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify features for machine learning and pattern classification in time-domain signals. Whether for medical imaging or structural health monitoring, it develops analysis techniques and measurement...
CRC Press, 2022. — 295 p. — ISBN 978-0-367-68545-4. Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and...
Pearson Education, 2019. — 282 p. — ISBN 978-0-13-411654-9. Foundational Hands-On Skills for Succeeding with Real Data Science Projects Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more...
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
Leanpub, 2022. — 352 p. This book provides a guided tour along the wide range of ML methods that have proven useful in process industry. Step-by-step instructions, supported with real process datasets, show how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, soft sensing, and process control This book provides an...
O’Reilly Media, Inc., 2021. — 482 p. — ISBN 9781098102364. 2021-07-21: First Release By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including...
Springer, 2020. — 285 p. — ISBN: 9811529094. This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the...
New York: Springer, 2020. — 195 p. This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based...
Palm Bay: CRC Press/Apple Academic Press, 2022. — 685 p. This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather...
Packt Publishing, 2023. — 240 p. Key Features: Master Conformal Prediction, a fast-growing ML framework, with Python applications. Explore cutting-edge methods to measure and manage uncertainty in industry applications. The book will explain how Conformal Prediction differs from traditional machine learning. Book Description: In the rapidly evolving landscape of machine...
Springer, 2018. — 93 p. — ISBN10: 331975713X; ISBN13: 978-3319757131. This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior. The author describes techniques to reduce significantly the time required for simulations involving...
BPB Publications, 2020. — 338 p. — ISBN 978-93-89845-365. An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and...
Manning Publications, 2024. — 576 p. — (MEAP v9). How do you know what might have happened, had you done things differently? Causal Machine Learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. In Causal AI you will learn how to: Build causal...
Manning Publications, 2022. — 358 р. — ISBN: 978-1617299797. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This...
New York: amazon.com Services LLC, 2020. — 76 p. Well, machine learning is becoming a widely-used word on everybody's tongue, and this is reasonable as data is everywhere, and it needs something to get use of it and unleash its hidden secrets, and since humans' mental skills cannot withstand that amount of data, it comes the need to learn machines to do that for us. So we...
Code Planet, 2025. — 231 p. Machine Learning with Python in this all-in-one guide designed for beginners and experienced developers alike! Whether you're diving into supervised and unsupervised learning, exploring neural networks, or mastering real-world applications, this book provides step-by-step explanations, hands-on examples, and expert insights. Machine Learning (ML) is...
John Wiley & Sons, Inc., 2019. — 366 p. — ISBN: 978-81-265-7990-7. This book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts...
Xlibris US, 2021. — 556 р. This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised Machine Learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector...
Springer Cham, 2023. — 451 p. — (Computational Methods in Engineering & the Sciences) — eBook ISBN: 978-3-031-36644-4. Comprehensive state-of-the-art book on scientific machine learning approaches in modelling & simulation Covers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modelling & material design Focuses on engineering applications in...
Packt Publishing, 2016. — 1043 p. — ISBN: 978-1-78712-857-6. Leverage benefits of machine learning techniques using Python. Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of...
Princeton University Press, 2024. — 393 p. Data Science methods and tools―including programming, data management, visualization, and Machine Learning―and their application to neuroimaging research. As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about...
BPB Publications, 2021. — 866 р. This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms...
2018. —374p. — ISBN: 1138492698. This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.
Independently published, 2022. — 240 p. — ISBN-10: B09HKHPT9M. Why was this book written? Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, AI Engineering being associated with machine learning. Our aim in this book is to demystify these concepts and provide...
Wiley-Scrivener, 2024. — 489 p. The book provides a comprehensive overview of current research and developments in the field of Deep Learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of Deep Learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep Learning helps...
Springer, 2020. — 292 p. — ISBN: 3030378292. This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power...
CRC Press / Chapman & Hall, 2023. — 228 p. — (Chapman & Hall/CRC Cyber-Physical Systems). — ISBN 9781032146416. Machine Learning, Cyber Security, and Blockchain in Smart Environment: Application and Challenges provides far-reaching insights into the recent techniques forming the backbone of smart environments, and addresses the vulnerabilities that give rise to the challenges...
Berkeley, CA: Apress, 2019. — 163 p. — ISBN: 978-1-4842-4297-1. Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products. Many applications on iPhone use...
Scatterplot Press, 2017. — 155 p. — ISBN: 1549617214. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey...
Scatterplot Press, 2017. — 155 p. — ISBN: 1549617214. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey...
Ryan Turner, 2019. — 144 p. Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python?This book will help you overcome those problems! As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve...
BPB Publications, 2023. — 302 р. — ISBN 978-93-89898-27-9. A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques. Key Features - Understand the core concepts and algorithms of Machine Learning. - Get started with your Machine Learning career with this easy-to-understand guide. - Discover different Machine Learning...
Springer, 2023. — 333 p. Transfer learning is one of the most important technologies in the era of Artificial Intelligence and Deep Learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and...
Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan. — Cambridge University Press, 2020. — 393 p. — ISBN: 978-1-107-01690-3. Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small...
Springer, 2021. — 204p. — (SpringerBriefs in Applied Sciences and Technology, SpringerBriefs in Computational Intelligence). — ISBN 978-981-33-4022-0. Network analysis has gained a lot of prominence over the past decade. This is because of a better understanding and control over learning representations of various entities like nodes, edges, subgraphs, cliques, and graphs that...
2nd ed. — Singapore: World Scientific Publishing Company, 2022. — 1055 p. — ISBN 9811245142. The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence . This field of research evolved so much in the last five years that it necessitates this new...
Apress, 2018. — 362 p. — ISBN: 1484235630. Code files only! Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in...
New York: Springer, 2021. — 197 p. This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and...
2nd Edition. — Springer, 2024. — 411 p. — ISBN 978-3-031-60949-7. This book is a concise but thorough introduction to the tools commonly used in pattern recognition and Machine Learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression. The Second Edition...
CRC Press, Taylor & Francis Group, LLC, Enrique Garcia Ceja, 2022. — 432 p. — (Chapman & Hall/CRC The R Series). — ISBN 978-1-032-06704-9. Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based...
Packt Publishing, 2019. — 334 p. — ISBN: 9781789807943. Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains Implement smart...
CRC Press, 2018. — 208 p. — ISBN: 1138593737. Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing...
Springer, 2008. — 296 p. Large collections of digital multimedia data are continuously created in different fields and in many application contexts. Application domains include web searching, cultural heritage, geographic information systems, biomedicine, surveillance systems, etc. The quantity, complexity, diversity and multi-modality of these data are all exponentially...
Ciml. info; TODO, 2015. — 227 p. This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course...
Leanpub, 2023. — 192 р. This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on...
Addison-Wesley Professional, 2019. — 592 р. — (Addison-Wesley Data & Analytics Series). — ISBN: 978-0134845623. The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyonewill help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an...
MDPI, 2019. — 256 p. The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The...
Springer Singapore, 2024. — 304 p. — eBook ISBN 978-981-97-3954-7. Explains basic theories without over explanation for readers to build machine learning models Guides readers step by step to use KNIME to practice machine learning Provides examples from Kaggle competitions in various fields This book guides professionals and students from various backgrounds to use machine...
Addison-Wesley Professional, 2018. — 256 p. — ISBN: 9780134863924, 0134863860 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you...
Springer, 2022. — 585 p. Electronic design automation (EDA) is a software technology that attempts to let computers undertake chip design tasks so that we can handle complexities beyond manual design capabilities. Although conventional EDA techniques have led to huge design productivity improvement, they face the fundamental limit that most EDA problems are NP hard and...
O’Reilly, 2016. — 79 p. — ISBN: 9781491956205. Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O’Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework...
Sonar Publishing, 2023. — 362 p. "Python for Machine Learning: From Fundamentals to Real-World Applications" is your comprehensive roadmap to mastering Machine Learning with Python. Whether you're a beginner looking to enter the exciting world of Data Science or an experienced programmer aiming to delve deeper into Machine Learning, this book provides you with the knowledge and...
2nd Edition. — Springer, 2023. — 279 p. The new edition of this popular professional book on Artificial Intelligence (ML) and Machine Learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective,...
Springer, 2020. — 180 p. — ISBN: 978-3-030-35970-6. This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the...
Pearson, 2019. — 288 p. — ISBN: 9780134116556. Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers...
Addison-Wesley Professional, 2025. — 224 p. — (Addison-Wesley Data & Analytics Series). — ISBN-13: 978-0-13-834074-2. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris...
Singapore: World Scientific Publishing Company, 2022. - 692 p. - ISBN 9811254176. Machine Learning (ML) has become a very important area of research widely used in various industries. This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably...
Springer, 2016. — 251 p. — (Studies in Big Data 7). — ISBN: 9783319069371, 9783319069388 The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms...
Academic Press, 2020. — 394 p. — ISBN: 978-0-12-815739-8. This book is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for...
Leanpub, 2023-02-14. — 101 р. This book teaches you how to quantify the uncertainty of machine learning models with conformal prediction in Python. Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification. Summary A prerequisite for trust in Machine Learning is uncertainty...
3rd Edition. — Apress Media LLC., 2024. — 463 р. — ISBN 978-1-4842-9846-6. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem...
Independently published, 2021. — 352 p. Hands-on and written in jupyter notebook...feel the heat! Data Science from Scratch (Part 2) This part is entirely focus on business machine learning. The book covers theory and complete pipeline along with hands-on exercise for widely used machine learning algorithms.
Apple Academic Press, 2022. — 264 p. — ISBN 978-1-003-05512-9. This new volume, Empowering Artificial intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next-big-game changer in research and technology, The...
Springer, 2022. — 222 p. — (Studies in Big Data 103). — ISBN 978-981-16-9157-7. Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and...
Nova Science Publishers, Inc., 2021. — 186 p. — ISBN 153619512X. The machine learning field is concerned with the question of how to create computer programs that automatically improve information. In recent years, many successful electronic learning applications have been made, from data mining systems that learn to detect fraudulent credit card transactions, filtering...
Packt, 2020. — 351 p. — ISBN: 9781839219061. Take a comprehensive and step-by-step approach to understanding machine learning Key Features Discover how to apply the scikit-learn uniform API in all types of machine learning models Understand the difference between supervised and unsupervised learning models Reinforce your understanding of machine learning concepts by working on...
Orange Education Pvt Ltd, AVA, 2024. — 411 p. — ISBN 978-81-97223-94-5. Максимальное машинное обучение с Scikit-Learn: раскройте возможности Scikit-Learn и Python для создания передовых приложений прогнозного моделирования и более глубокого понимания машинного обучения Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. “Ultimate...
Independently published, 2023. — 176 p. In the past decade, Artificial Intelligence has been making waves. From self-driving cars to Siri to Alexa, Artificial Intelligence (AI) is everywhere. But what exactly is it? The term "artificial intelligence" was coined in 1956 by John McCarthy, a computer scientist at Dartmouth College. His work was based on the idea that computers...
Morgan & Claypool, 2012. — 114 p. — ISBN: 978-1608457250 The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "Oracle" (e.g., a human annotator) that already...
Springer, 2023. — 175 p. — (Engineering Optimization: Methods and Applications). — ISBN 978-981-99-7455-9. This book aims to provide a collection of state-of-the-art scientific and technical research papers related to Machine Learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering...
Wiley-Scrivener Publishing, 2021. — 305 p. — ISBN 978-1-119-76885-2. Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine...
Springer, 2014. — 162 p. — ISBN: 978-3-658-04936-2, e-ISBN: 978-3-658-04937-9. Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. However, motor skills are not easy to learn – babies require several month to develop proper grasping skills. Learning motor skills is also an active research topic in robotics. However,...
CRC Press, 2024. — 494 p. Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called “ADAM” (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to...
Packt Publishing, 2023. — 334 p. The AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your own business with cutting-edge emotion analysis, you’ve found what you need in this trusty guide. In Machine Learning for Emotion Analysis, you’ll take your foundational...
Birmingham: Packt Publishing, 2019. — 338 p. Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and...
Leanpub, 2019. — 133 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
Reactive Publishing, December 28, 2023. — 371 p. Dive into the world of Artificial Intelligence with "Unsupervised Machine Learning with Python," the essential guide forprofessionals eager to master the most sophisticated analysis skills and unlock new dimensions of data interpretation. Building on the knowledge foundation of those who have already ventured into the realm of...
Springer, 2023. — 457 p. — ISBN 3031333411. This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of Machine Learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its...
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
Пер. с англ. — СПб.: БХВ-Петербург, 2022. — 450 с.: ил. — ISBN 978-5-9775-6797-8. Приводимые в книге паттерны проектирования отражают лучшие практические подходы к решению типичных задач машинного обучения. Указанные паттерны, реализованные в программном коде, сконцентрировали опыт сотен экспертов в простые и легкодоступные советы. Книга содержит подробный разбор 30 паттернов,...
Учебное пособие. — Чебоксары: Среда, 2024. — 140 с. — ISBN 978-5-907830-17-2. Метаэвристические алгоритмы, инспирированные природой, являясь одним из направлений в искусственном интеллекте и машинном обучении, в последние десятилетия стали мощным инструментом оптимизации, широко используются при распознавании образов и компьютерном зрении, технической и медицинской диагностике,...
Springer, 2020. — 506 p. — ISBN: 978-3-030-40344-7 (eBook). This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced...
O’Reilly, 2018. — 366 p. This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and...
Springer, 2023. — 763 p. — (Genetic and Evolutionary Computation). — ISBN 9819938139. This book, written by leading international researchers of evolutionary approaches to machine learning , explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts . The first part...
Springer, 2017. — 97 p. — (SpringerBriefs in Economics). — ISBN: 978-3-319-69013-1. This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing...
Packt Publishing, 2018. — 576 p. — ISBN: 978-1788621113. Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their...
Packt, 2020. — 256 p. — ISBN: 9781789801781. Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular machine learning...
Manning Publications, 2023. — 335 p. Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your Machine Learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing...
Pearson, 2018. — 456 p. — ISBN: 9353066697. Readers of this book will gain a thorough understanding of machine learning concepts. Not only students but also software professionals will find a variety of techniques with sufficient discussions in this book that cater to the needs of the professional environments. Technical managers will get an insight into weaving machine...
Elsevier, 2023. — 420 p. Advanced Machine Learning Techniques includes the theoretical foundations of modern Machine Learning, as well as advanced methods and frameworks used in modern Machine Learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an...
3rd Edition. – O’Reilly Media, 2023. –- ISBN 978-1-098-12597-4. Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal...
New York: Springer, 2019. — 372 p. This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn...
O’Reilly Media, Inc., 2020. — 366 p. — ISBN: 978-1-492-05319-4. Code files only! Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll...
CRC Press, 2023. — 346 p. — eBook ISBN: 9781003187158. Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and...
Springer, 2020. — 157 p. — (Computer Architecture and Design Methodologies). — ISBN: 978-981-13-3322-4. This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware...
O’Reilly Media, 2017. — 237 р. — ISBN: 978-1-491-92413-6. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
Springer, 2018. - 161p. - ISBN: 3030001288 This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the...
Packt Publishing Ltd, 2025. — 485 p. Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine...
Independently published, 2023. — 520 p. — ASIN: B0CP11GTC1 (converted to PDF). "Machine Learning in Python for Everyone" is your comprehensive guide to mastering machine learning with the Python programming language. Whether you're a novice looking to embark on your data science journey or an experienced practitioner aiming to refine your skills, this book provides a structured...
New York: Springer, 2017. — 138 p. — ISBN: 978-3-319-55312-2 This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results...
Leanpub, 2023. — 208 p. This book provides a comprehensive coverage of Machine Learning (ML) methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show (using Python) how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods....
Boca Raton: CRC Press, 2022. — 232 p. Number of approaches are being defined for statistics and Machine Learning. These approaches are used for the identification of the process of the system and the models created from the system's perceived data, assisting scientists in the generation or refinement of current models. Machine Learning (ML) is being studied extensively in...
No Starch Press, 2021. — 291 p. — ISBN 978-1-7185-0056-3. A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial...
CRC Press, 2019. — 350p. - ISBN: 0815356226 Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and...
Springer, 2023. - 529 p. - ISBN 981993916X. This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods . It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy...
BPB Publications, 2023. — 271 p. Learn how to use AutoML to leverage Machine Learning for solving business problems. Key Features - Get familiar with the common machine learning problems and understand how to solve them. - Understand the importance of different types of data and how to work with them effectively. - Learn how to use machine learning and AutoML tools to solve...
Boca Raton: CRC Press, 2023. — 464 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN 1032462140. The release of ChatGPT has kicked off an arms race in Machine Learning (ML) , however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in...
Medium, 2017. — 97 p. This guide is intended to be accessible to anyone . Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series. If you're more interested in figuring out which courses to take, textbooks to read, projects to attempt, etc. Take...
IGI Global, 2022. — 305 p. — (Advances in Systems Analysis, Software Engineering, and High Performance Computing). — ISBN 1799883507. Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and...
New York: lulu.com, 2020. — 255 p. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like...
Leanpub, 2023. — 216 p. Master Machine Learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your Machine Learning applications. Machine Learning is transforming fields from healthcare diagnostics to climate change predictions through their predictive performance. However, these complex Machine...
De Gruyter, 2023. — 506 р. — ISBN 978-3-11-078593-7. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 364 р. — ISBN 978-3-11-078595-1. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
Wiley, 2023. — 610 p. Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of Artificial Intelligence (AI), Machine Learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and...
Wiley, 2020. — 464 p. — ISBN: 1119591511. Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning―a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions―allows organizations to...
Manning Publications, 2022. — 358 р. — ISBN: 978-1617299797. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This...
Springer, 2021. — 177 p. — (Studies in Big Data, 87). — ISBN 981336517X. This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT . The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge...
Springer, 2022. - 143 p. - (Machine Learning: Foundations, Methodologies, and Applications). - ISBN 9811968969. This book offers a brief but effective introduction to quantum machine learning (QML) . QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing....
Springer, 2021. — 197 p. — ISBN 978-3-030-72068-1. This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical...
New York: Packt Publishing, 2016. — 983 p. Learn to solve challenging data science problems by building powerful machine learning models using Python. Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we...
O’Reilly Media, Inc., 2023. — 303 p. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure,...
New York: Autoedition, 2014. — 261 p. This course will focus on theoretical aspects of Statistical Learning and Sequential Prediction. Until recently, these two subjects have been treated separately within the learning community. The course will follow a unified approach to analyzing learning in both scenarios. To make this happen, we shall bring together ideas from probability...
IGI Global, 2017 - 129 p. Software development and design is an intricate and complex process that requires a multitude of steps to ultimately create a quality product. One crucial aspect of this process is minimizing potential errors through software fault prediction. Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities is an innovative...
3rd edition. — Springer, 2023. — 975 p. — ISBN 978-3-031-24627-2. This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects...
CRC Press, 2021. — 221 p. — ISBN 9780367703868. Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This...
Springer, 2020. — 221 p. — ISBN: 9811536880. The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics...
Packt, 2019. — 252 p. — ISBN: 1838828974. Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step approach to improving...
O’Reilly, 2024. — 332 p. Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In this hands-on guide, author Anthony...
Manning Publications Co., 2019. — 280 p. — ISBN: 978-1617293702. MEAP Version 7 It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine...
New York: Morgan and Claypool, 2022. — 123 p. This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and...
Springer, 2024. — 357 p. — (Studies in Big Data). This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data...
Scrivener Publishing, 2022 — 480 p. — ISBN: 1119821258. The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software...
2nd edition. — CRC Press, 2023. — 498 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — eBook ISBN 978-1-003-26487-3. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic...
Josh Starmer, 2022. — 305 p. Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each concept is clearly...
Academic Press, 2021. — 324 p. — ISBN 978-0-12-818279-6. Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the...
World Scientific, 2018. — 328 p. — ISBN 9813271221. This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality....
AI Publishing LLC, 2020. — 302 p. — ISBN: 978-1-7347901-5-3. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare,...
Reactive Publishing, 2023. — 365 р. — ISBN: B0CQX39XGV. Push the boundaries of machine learning with Python and elevate your data analysis skills to new heights! 'Supervised Machine Learning with Python' is the essential guide for professionals who have mastered the basics and are ready to dive into the more complex and powerful aspects of machine learning. If you were...
Berlin: Walter de Gruyter, 2020. — 152 p. — (De Gruyter STEM). — ISBN: 3110595532. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on. From the preface: "With numerous books focusing on machine learning and visual...
Springer, 2012. — 331 p. It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing...
Самиздат, 2022. — 106 с. Чему Вы научитесь: Строить дашборды и писать SQL запросы. Оценивать влияние моделей на показатели бизнеса с помощью A/B-тестов. Деплоить модели и создавать свои микросервисы для ML
Учебно-методическое пособие. — Санкт-Петербург: Университет ИТМО, 2024. — 71 с. В учебно-методическом пособии приводится базовая теория и примеры некоторых способов разметки, и обогащения данных различных модальностей. В главах 1-7 подробно рассмотрены понятия аннотационной схемы для разметки данных, корпуса “золотой стандарт”, а также описаны методики и рекомендации по...
СПб.: Питер, 2025. — 608 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-4191-3. Машинное обучение давно уже стало синонимом искусственного интеллекта. Оно проникло во многие аспекты нашей жизни и стало одной из важнейших областей современной науки. Эта книга — путеводитель по ключевым идеям машинного обучения. Вы узнаете, как методы машинного обучения получаются из...
Учебное пособие по курсам «Модели и методы инженерии знаний», «Методы машинного обучения». — Ростов-на-Дону; Таганрог: Южный федеральный университет, 2022. — 105 с. — ISBN 978-5-9275-4215-4. Пособие посвящено рассмотрению теоретических подходов к извлечению информации из текстов, рассмотрению и систематизации прогностических методов и моделей в маркетинге и интернет-рекламе, на...
AI Publishing, 2020. — 308 p. — ISBN B08QJMNVCX. 10 Machine Learning Projects Explained from Scratch Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful...
Akshay B R, Sini Raj Pulari,T S Murugesh, Shriram K Vasudevan. — CRC Press, 2025. — 259 р. — ISBN: 978-1-032-67666-1. Machine Learning is an exciting and rapidly growing field which is all about creating algorithms and models that enable computers to recognize patterns, make predictions, and improve their performance over time. It empowers computers to learn from data and...
Springer, 2023. — 314 p. A critical challenge in Deep Learning is the vulnerability of Deep Learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in...
Independently published, 2024. — 136 p. (converted to PDF) This comprehensive guide is tailored to address precisely those concerns and assist you in navigating through the intricacies of Python Machine Learning. In "Python Machine Learning: A Comprehensive Beginner's Guide with Scikit-Learn and Tensorflow," you will embark on a journey to unravel the mysteries of:...
Manning Publications, 2025. — 376 p. — ISBN-13: 978-1633438750. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside,...
De Gruyter, 2024. — 486 p. This book explains how to use the programming language Python to develop Machine Learning and Deep Learning tasks. Machine Learning (ML) is a discipline within the field of Artificial Intelligence (AI) that concentrates on the creation of algorithms and models, allowing computer systems to acquire knowledge and make forecasts or choices without the...
Packt Publishing, 2016. — 50 p. It is a well-established fact that we, as human beings, learn through experience. During our early childhood, we learn to imitate sounds, form words, group them into phrases, and finally how to talk to another person. Later, in elementary school, we are taught numbers and letters, how to recognize them, and how to use them to make calculations...
Wiley-IEEE Press, 2024. — 240 p. Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations. Machine Learning Applications discusses methodological advancements of Machine Learning and Deep Learning, presents applications in image processing, including face and vehicle detection, image...
Bentham Science Publishers, 2023. — 139 p. — eISBN: 978-981-5124-42-2. Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications,...
Anderson Coen, 2019. — 180 p. What is machine learning? Does it really help businesses provide better services and earn more? How can I improve my business processes to increase ROI (Return On Investment)? I am unable to focus on important tasks because I am getting bogged down by menial tasks. If you are confronted by one or more of these questions, this book is for you! You...
Packt Publishing, 2016. — 444 p. — ISBN: 978-1-78439-475-2. Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the...
O’Reilly Media, 2011. — 148 p. — ISBN: 9781449314309. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation. This book also includes a short tutorial on...
Wiley-IEEE Press, 2025. — 445 р. — ISBN: 978-1394272945. Enables readers to develop foundational and advanced vectorization skills for scalable Data Science and Machine Learning and address real-world problems. Offering insights across various domains such as Computer Vision and natural language processing (NLP), Vectorization covers the fundamental topics of vectorization...
CRC, 2020. — 262 p. — ISBN 9780367503369. The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature...
CRC Press, 2024. — 344 p. — eBook ISBN: 978-1-003-24019-8. Providing an essential and unique bridge between the theories of signal processing, machine learning, and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and...
Second updated edition. — O’Reilly Media, Inc., 2019. — 901 p. - ISBN: 978-1-492-03264-9. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you...
Independently published, 2019. — 146 p. — ISBN-13 978-1089401513. What is machine learning? How machine learning works? Should I use a machine learning model or another approach to solve my problem? How do I implement machine learning to my problem? What are the machine learning methods I can use for my problem? How do I know my machine learning model is efficient? Are you...
O’Reilly Media, October 2019. — 62 p. — ISBN: 9781098115470. Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate, but can also make their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers,...
Basel: MDPI AG, 2021. — 186 p. The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of Computer Science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical...
Packt Publishing, 2016. — 625 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689 This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working examples using a wide...
CRC Press, 2019. — 364 p. — ISBN 13 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
Amazon.com Services LLC., 2019. — 168 p. — ISBN: 1706955286, ASIN B0818Z9Z7C. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Machine Learning, from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you'll discover everything you need to know to get started with machine learning, and begin...
New York: O’Reilly Media, Inc., 2021. — 112 p. Who Should Read This Book What Readers Will Learn Preliminary Book Outline Bringing it All Together Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Contemporary Model Governance Basic Legal Obligations AI Incidents Organizational and Cultural Competencies for Responsible AI...
O’Reilly Media, 2020. — 366 p. — ISBN: 1492053198. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools...
Apress, 2021. — 300 p. — ISBN-13 978-1-4842-7031-8. Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book...
Frank Hutter, Lars Kotthof, Joaquin Vanschoren. — Springer, 2019. — 219 p. — ISBN: 978-3-030-05318-5. В этой книге представлен первый всеобъемлющий обзор общих методов автоматического машинного обучения (AutoML), собраны описания существующих систем на основе этих методов и обсуждена первая серия международных проблем систем AutoML. Недавний успех коммерческих приложений...
CRC Press, 2022. — 207 p. This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity. This bookfocuses on real-world industrial applications and case...
New York: Springer, 2019. — 262 p. This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and...
O’Reilly Media, Inc., 2023. — 143 p. — (Third Early Release). — ISBN 978-1-492-09761-7. Whether based on academic theories or Machine Learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of...
New York: Springer, 2017. — 150 p. This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and Machine Learning (ML). Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive...
Bentham Books, 2022. — 240 p. — ISBN 978-981-5079-19-7. This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it...
Scientific Books, 2022. — 257 p. In this book, supervised learning techniques related to regression will be developed. More specifically, we will go deeper into the linear models, LASSO regression, LARS LASSO regression, RIDGE Regression, Least Angle Regression, Multitask LASSO regression, Elastic Net Regression, Multi task Elastic Net Regression, SGD Regression, Support Vector...
Wiley-Scrivener, 2025. — 403 p. — ISBN 978-1-394-27255-6. Глубокое обучение с подкреплением и примеры его промышленного использования: искусственный интеллект для реальных приложений “Deep Reinforcement Learning and its Industrial Use Cases: Harnessing AI for Real-World Applications” is an essential guide that supplies complex theories, practical insights, and diverse case...
IGI Global, 2020. — 364 p. — ISBN 9781799830955. Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a...
IGI Global, 2024. — 473 p. — ISBN 9781668485330. Machine Learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these...
Academic Press/Elsevier, 2023. — 222 p. Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The...
CRC Press, 2022. — 212 p. — ISBN: 978-0-367-63453-7. The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature,...
Wiley, 2020. — 336 p. — ISBN: 978-1-119-60290-3. Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine...
Basel: MDPI AG, 2020. — 302 p. This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity...
O’Reilly, 2017. — 388 p. — ISBN: 978-1-491-91721-3. Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the...
Springer, 2023. — 101 p. — (SpringerBriefs in Computer Science). This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and...
Leanpub, 2023. — 68 p. Reinforcement Learning: Unleashing the AI Agent's Power" Discover the thrilling world of Reinforcement Learning (RL) in our engaging eBook! Learn the fundamentals of RL, from AI agents and environments to rewards and actions. Explore real-world applications like robotics, healthcare, and personalized recommendations, where RL is transforming industries....
2018. — 54 p. Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core principles that will make it easier to step to a more advanced book should you want to learn more.
Manning Publications, 2021. — 250 p. — ISBN 1617297763, 9781617297762. Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to...
Apress Media LLC, 2022 — 347 p. — ISBN-13: 978-1-4842-7920-5. Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep...
Springer, 2001 — 324 p. — ISBN13: 978-3540424901 На англ. языке. In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry,...
2nd edition, Apress, 2021. — 428 p. — ISBN 148426536X, 9781484265369. This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine...
BISAC: Computers / Intelligence (AI) & Semantics, 2018. — 394 p. I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook". The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and...
Chapman and Hall/CRC, 2023. — 405 p. — ISBN 13 9781003306566. Энтропийная рандомизация в машинном обучении Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with...
IOP Publishing, 2023. — 234 p. This book presents Machine Learning (ML) concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics...
Springer, 2013. — 506 p.
This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the...
Packt Publishing, 2024. — 280 p. Authors: Stephanie Rivera, Anastasia Prokaieva, Amanda Baker, Hayley Horn Key Features: Build machine learning solutions faster than peers only using documentation Enhance or refine your expertise with tribal knowledge and concise explanations Follow along with code projects provided in GitHub to accelerate your projects Book Description:...
Cham: Springer International Publishing, 2018. — 353 p. — ISBN: 978-3-319-99492-5. This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning:...
Apple Academic Press, CRC Press, 2025. — 353 р. — ISBN: 978-1-77491-639-1. This new book provides an in-depth understanding of Federated Learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how Federated Learning (FL) integrates AI technologies, such as...
ITexLi, 2021. — 131 p. — ISBN 183969484X 9781839694844 1839694866 9781839694868. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers,...
CRC Press, 2024. — 99 p. Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where...
MIT Press, 2012. — 263 p. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural...
Apress, 2021. — 193 p. — ISBN 978-1484268421. Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable...
Springer, 2022. — 203 p. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.
Leanpub, 2020. — 220 p. The software industry is experiencing a boom in ML development and usage. This is not unlike previous software engineering booms in the early 2000s. The current boom manifests itself with a menagerie of constructs, abstractions, frameworks, and workflows. This multitude of integration challenges remind us of old and classical software problems. Some of...
Independently published, 2020. — 302 p. — ISBN B08NZW37N6. 4 Books in 1! Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading...
Tan Publishing, 2023. — 217 р. Упрощенные алгоритмы машинного обучения Machine Learning Simplified: A Comprehensive Guide to Supervised, Unsupervised, and Semi-Supervised Algorithms. In today's data-driven world, Machine Learning has become an essential tool for businesses and organizations of all sizes. This powerful technology enables computers to learn from data without...
Academic Press, 2025. — 1220 p. Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods,...
Manning, 2023. — 352 p. Discover all-practical implementations of the key algorithms and models for handling unlabelled data. Full of case studies demonstrating how to apply each technique to real-world problems. The book introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll...
BPB Publications, 2018. — 266 p. — ISBN: 9386551934. This book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the...
Boca Raton: CRC Press, 2022. — 93 p. This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning...
Princeton: Princeton University Press, 2023. — 277 p. — ISBN 069120392X. A hands-on introduction to machine learning and its applications to the physical sciences. As the size and complexity of data continue to grow exponentially across the physical sciences , machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances...
Springer, 2018. — 493 p. — ASIN B07BKQ1K1F. Artificial Intelligence, Data Mining Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: -...
IGI Global, 2023. — 385 p. — ISBN-13: 978-1668456439. In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address...
CRC Press, 2023. — 409 p. — ISBN 978-0-367-77205-5. Стратегии после усадки в статистическом и машинном обучении для данных высокой размерности This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in Data Science. It combines statistical learning and Machine Learning techniques in a unique and optimal way....
O’Reilly, 2020. — 260 p. — ISBN: 978-1-492-04510-6. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn...
Independently published, 2024. — 331 p. — ASIN : B0CW1C5S62. Many believe that a machine learning model, once trained, can act autonomously. This misconception has hindered innovation in ML/AI for far too long. In reality, ML models require integration within a comprehensive system encompassing inputs, processing, and outputs. My new book, "Applied Machine Learning: A Practical...
Dordrecht: Springer Science+Business Media B.V, 2011. — 607 p. — ISBN: 978-90-481-9419-3. A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering...
Princeton: LN, 2023. — 256 p. Supervised Learning Linear Regression: An Introduction A Warm-up Example Using Linear Regression for Sentiment Prediction Importance of Featurization Linear Regression in Programming Statistical Learning: What It Means to Learn A Warm-up Example Summary of Statistical Learning Implications for Applications of Machine Learning Optimization via...
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur. — 2nd Edition. — Packt Publishing, 2020. — 488 p. — ISBN: 978-1-80020-904-6. Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing....
Academic Press / Elsevier, 2023. — 348 p. — ISBN 978-0-323-96098-4. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and...
Singapore: World Scientific Publishing Company, 2024. — 545 p. — ISBN 981129254X. Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorithms and finally system implementation in three...
CRC Press, 2020. — 488 p. — ISBN-13: 978-1-138-49568-5. Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R,...
Springer, 2023 — 295 p. — (Applied Mathematical Sciences). The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider's view of how to combine two mature fields, scientific computing and...
Independently published, 2022. — 98 p. — ASIN: B09QYQC31S. Have you thought about a career in data science? It’s where the money is right now, and it’s only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it’s the next logical step. Machine learning is a subsection...
Academic Press, 2022. — xiv, 284 p. — ISBN 978-0-12-824020-5. Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark...
Packt Publishing, 2019. - 334p. - ISBN: 9781789807943 !Code files only Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains...
Springer, 2021. — 173 p. — ISBN 978-3-030-70678-4. This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The...
Independently published, 2020. — 210 p. — ISBN: 979-8611346952. Would you like to learn how to use Python to generate machine learning models but you think it would be too difficult? Or perhaps you want to automate simple things with your computer but you don’t know how to do it? Here’s the deal… As a beginner you might think that programming is complex… Learning artificial...
Cambridge University Press, 2022. — 408 p. — ISBN 9781009128490. This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine...
CRC Press, 2024. — 289 p. — ISBN 978-1-032-39523-4. Machine Learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards Computer Science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of Machine Learning and look ahead to applications and...
Packt Publishing, 2017. — 438 p. — ISBN: 978-1788294041. An effective guide to using ensemble techniques to enhance machine learning models Key Features Learn how to maximize popular machine learning algorithms such as random forests, decision trees, AdaBoost, K-nearest neighbor, and more Get a practical approach to building efficient machine learning models using ensemble...
O’Reilly, 2020. — 166 p. — ISBN: 1492072745. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand...
New York: Springer, 2019. — 492 p. Machine learning methods are now an important tool for scientists, researchers, engineers, and students in a wide range of areas. Many years ago, one could publish papers introducing (say) classifiers to one of the many fields that hadn’t heard of them. Now, you need to know what a classifier is to get started in most fields. This book is written...
Springer, 2022. — 169 p. — ISBN 978-3-031-14633-6. Анализ данных с помощью машинного обучения для психологов: ускоренный курс по изучению Python 3 и машинного обучения за 10 часов The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of...
Packt Publishing, 2016. — 614 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689. This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working examples using a wide...
McGraw-Hill Education, 2018. — 650 p. — ISBN13: 978-93-5316-025-8. This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent...
Independently published, 2024. — 107 p. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
Apress, 2017. — 204 p. Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time...
Springer, 2020. — 640 p. — ISBN 3030593371. This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and...
Elsevier Inc., 2022. — 234 p. — ISBN 978-0-12-818721-0. Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific...
Springer, 2020. — 290 p. This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various statistical and machine learning methods, including K nearest neighbours, Bayesian...
Packt Publishing, 2019. — 456 p. — ISBN: 1789136364. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind...
2nd Revised Edition. — Cambridge University Press, 2025. — 426 p. — ISBN-13: 978-1-316-51886-1. As Machine Learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers...
Springer, 2022. - 226 p. - (Machine Learning: Foundations, Methodologies, and Applications). - ISBN 9811970823. This book introduces readers to the fundamentals of and recent advances in federated learning , focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning...
HiTeX Press, 2024. — 542 p. "Fundamentals of Machine Learning: An Introduction to Neural Networks" is an accessible yet comprehensive guide designed for individuals new to the intriguing world of Machine Learning. This book meticulously unfolds the foundational principles and techniques in Machine Learning while placing a keen emphasis on neural networks. Readers are led...
Cambridge University Press 2000 ISBN9781107043466. Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
CRC Press, 2025. — 300 p. — ISBN 978-1-032-55232-3. This book presents the research into and application of Machine Learning in quantum computation, known as Quantum Machine Learning (QML). It presents a comparison of Quantum Machine Learning, classical Machine Learning, and traditional programming, along with the usage of quantum computing, toward improving traditional Machine...
O’Reilly Media, 2017. — 67 р. — ISBN: 978-1-491-92406-8. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
Newark: John Wiley & Sons, Incorporated, 2018. — 330 p. Title page Copyright Organization How to Use This Book About the Companion Website Introductionâ Examples from Real Life The Problem of Learning Domain Range Data Loss Risk The Reality of the Unknown Function Training and Selection of Models, and Purposes of Learning Notation Regression General Framework Loss Estimating...
BPB Publications, 2022. — 751 p. — ISBN 978-93-91030-42-1 Master the ML process, from pipeline development to model deployment in production. Key Features Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. A step-by-step approach to cover every data science task with utmost efficiency and highest performance. Access...
Springer, 2021. — 341 p. — (Studies in Big Data). — ISBN 978-981-15-9491-5. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in...
Packt, 2019. - 298p. - ISBN: 1789612853 Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in...
O’Reilly Media, 2020. — 232 p. — ISBN: 978-1-492-07277-5. Your company creates terabytes and even petabytes of data, but are you actually putting it to work? The majority of enterprises stumble on their way to becoming data driven. Machine learning promises to reverse that trend, and early adopters are already seeing the benefits with increased ROI. In this report, author Alice...
Singapore: World Scientific, 2022. — 249 p. The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This...
Cambridge: Cambridge University Press, 2022. — 351 p. — ISBN 1108843603. This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic...
River Publishers, 2025. — 172 р. — ISBN978-87-7004-653-4. This is an essential resource for beginners and experienced practitioners in Machine Learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow,...
Apress, 2021. — 192 p. — ISBN 978-1484271094. Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern...
Springer Berlin Heidelberg, 2011. — 262 p. — ISBN: 978-3-642-22910-7. This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010,...
Springer, 2021. — 769 p. — ISBN 978-3-030-70541-1. This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and...
Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli. — Apress Media LLC., 2020. — 320 p. — ISBN13: (electronic): 978-1-4842-6094-4. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow...
O’Reilly Media, 2022. — 425 p. — ISBN 978-1-492-09805-8. While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest,...
Springer, 2023. — 371 p. — (Adaptation, Learning, and Optimization 27). — ISBN 978-3-031-11747-3. Федеративное и трансфертное обучение This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of Federated...
BPB Publications, 2021. — 427 p. — ISBN 9389328624, 9789389328622 Beginner's guide to explore machine learning in banking and finance. The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this...
Department of Electrical Engineering and Computer Sciences University of California at Berkeley, 2017. — 164 p. Linear Classifiers and Perceptrons Perceptron Learning; Maximum Margin Classifiers Soft-Margin Support Vector Machines; Features Machine Learning Abstractions and Numerical Optimization Decision Theory; Generative and Discriminative Models Gaussian Discriminant...
3rd ed. — Singapore: World Scientific Publishing Company, 2024. — (Series on Intelligence Science, 5). — 390 p. — ISBN 9811293988. This third edition comprehensively captures the cutting-edge research achievements of AI. Topics are thoroughly revised and updated , presenting the latest techniques and strategies to address the impending challenges facing computer scientists...
Cambridge: Cambridge University Press, 2022. - 601 p. - ISBN 1316512827. This self-contained introduction to machine learning, designed from the start with engineers in mind , will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems . With a consistent emphasis on the connections between...
Packt Publishing Ltd., 2020. — 404 p. — ISBN: 978-1-78995-608-5. Use the power of deep learning with Python to build and deploy intelligent web applications When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you’ll cover the latest tools and technological practices that are being used to implement deep learning in web...
Cham: Springer I, 2017. — 336 p. — ISBN: 978-3-319-47194-5. The topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented as a valid metaphor...
John Wiley & Sons, Inc., 2025. — 656 р. — ISBN-13: 978-1394155378. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering...
MDPI, 2020. — 110 p. This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with...
3rd edition. — Scatterplot Press, 2021. — 191 p. — ISBN B08RWBSKQB. Featured by Tableau as the first of “7 Books About Machine Learning for Beginners.” Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile? Well, hold on there… Before you embark on your journey, there are some high-level theory and...
Leanpub, Giannis Tolios, 2022. — 107 p. Упрощение машинного обучения с помощью PyCaret: подход с минимальным кодом для начинающих и экспертов! A beginner-friendly introduction to Machine Learning (ML) with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of Artificial Intelligence (AI), by easily training and deploying...
World Scientific, 2019. — 322 p. — ISBN 9811205663. Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical...
Springer, 2024. — 365 p. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of Machine Learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind...
Reactive Publishing, 2024. — 497 p. — (Python Libraries for Finance Book 9). — ASIN: B0D66BR1B1. "Machine Learning: Scikit-Learn for Finance" bridges the gap between the complex world of machine learning and practical financial applications. With a focus on hands-on examples and real-world scenarios, this book is designed to equip readers with the skills to implement...
Birmingham: Packt Publishing, 2023. — 423 p. — ISBN 1837634084. Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3. Key Features Learn to diagnose the need for regularization in any machine learning model. Regularize different ML models using a variety of techniques and...
2nd Edition. — Springer, 2022. — 490 p. — ISBN 978-3-031-06649-8. This book is about conformal prediction, an approach to prediction that originated in Machine Learning. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described ? conformal predictors ? are provably valid in the sense that they...
Packt Publishing, 2020. — 311 p. — ISBN 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and...
CRC Press, 2023. — 473 p. — (Textbooks in Mathematics) — eBook ISBN: 978-0-367-42551-7. The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of...
Boca Raton: CRC Press, 2023. — 468 p. — ISBN 1498756816. Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based...
Wiley-IEEE, 2023. — 283 p. Extensive Resource for Understanding Key Tasks of Network and Security Management. AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly...
The Institution of Engineering and Technology, 2021. — 261 p. — (IEM Computing Series, 39). — ISBN 978-1839530814. The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide...
Springer, 2024. — 141 p. In both the database and Machine Learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as “dirty data.” Clearly, for a given data mining or Machine Learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this...
Астана: Фолиант, 2024. — 688 с. — ISBN 978-601-11-0034-2. Предисловие : Дмитро Джулгаков, ведущий специалист PyTorch Core. Исчерпывающее руководство по машинному (МО) и глубокому обучению с использованием языка программирования Python, фреймворка PyTorch и библиотеки scikit-learn. Рассмотрены основы МО, алгоритмы для задач классификации, классификаторы на основе scikit-learn,...
AI Sciences LLC, 2018. — 132 р. — ASIN B07GMVP3WP. Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the...
O’Reilly Media, Inc., 2018. — 366 p. — ISBN: 978-1-491-98938-8. Целевая аудитория: опытные разработчики. Пожалуй, ни для кого не новость, что Python в последнее время популярен именно как инструмент для разработки в области машинного обучения и Data Science. Это руководство рассматривает современные подходы языка к решению актуальных проблем в данной области, принятые стандарты...
Boca Raton: CRC Press, 2024. — 169 p. — ISBN 1032515813. Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand...
Springer, 2024. — 163 p. This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 420 p. — ISBN: 978-1-119-64214-5. Code files only! Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques...
Packt Publishing, 2019. — 168 p. — ISBN: 978-1838550356, 1838550356. This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Key Features Your handy guide to building...
Packt Publishing, 2019. — 386 p. — ISBN: 978-1789348279, 1789348277. Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to...
Apress, 2018. — 690 p. — ISBN: 9781484233573. !Code files Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so...
Academic Press/Elsevier, 2024. — 520 p. Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. !Only code files Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine...
Packt, 2018. — 406 p. — ISBN: 978-1788623223. Develop your own Python-based machine learning system. Discover how Python offers multiple al Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support....
New York: Springer, 2016. — 229 p. Guides the reader from the control of simple dynamical systems to real-world experiments assisted by ample supplementary material Contains interviews with leading experts in the field Offers extensive color figures with clear explanations Includes exercises at the end of every chapter and MatLAB codes for all examples This is the first...
Springer, 2023. — 253 p. — (Data Science, Machine Intelligence, and Law 02). — ISBN 978-3-031-19666-9. This book gives a comprehensive overview of the state of Artificial Intelligence (AI), especially Machine Learning (ML) applications in public service delivery in Estonia, discussing the manifold ethical and legal issues that arise under both European and Estonian law. Final...
Springer, 2023. — 231 p. — (Computational Intelligence Methods and Applications). — ISBN 978-981-19-3887-0. Метаэвристика для машинного обучения: новые достижения и инструменты Using metaheuristics to enhance Machine Learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule...
Издание авторов. 2016. — 578 p. This Book is form the courses of two professional Data Scientists Kirill Eremenko from SuperDataScience and Hadelin de Ponteves from BlueLife AI. You can get a quick overview on Machine Learning & Deep Learning from this book. Also this book will be the best guide for the Courses of Kirill Eremenko and Hadelin de Ponteves. It can also helpful for...
Springer, 2024. — 283 p. This book explores the ongoing debate between shallow and Deep Learning in the field of Machine Learning. It provides a comprehensive survey of Machine Learning methods, from shallow learning to Deep Learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions...
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to...
Springer, 2024. — 393 p. — (Lecture Notes in Statistics 227). — ISBN 978-3-031-66252-2. This book offers a leisurely introduction to the concepts and methods of Machine Learning. Readers will learn about classification trees, Bayesian learning, neural networks and Deep Learning, the design of experiments, and related methods. For ease of reading, technical details are avoided...
Leanpub, 2022-08-24. — 52 р. Learn how to implement various feature selection methods in a few lines of code utilizing the open-source Python library Feature-engine. Feature-engine is an open-source Python library for feature engineering and feature selection. It uses Pandas and Scikit-learn under the hood to engineer and select feature subsets. Feature selection is the process...
KJ Publishing, 2022. — 166 p. Machine Learning is a complex subject area. Our goal in this lesson is to introduce you to some of the most common terms and ideas used in Machine Learning. I will then walk you through the different steps involved in Machine Learning (ML) and finish with a series of examples that use Machine Learning to solve real-world situations. Machine...
Second updated edition. — O’Reilly Media, Inc., 2019. — 901 p. - ISBN: 978-1-492-03264-9. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you...
3rd Edition. – O’Reilly Media, 2023. –- ISBN 978-1-098-12597-4. Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal...
O’Reilly Media, 2018. — 45 p. — ISBN: 9781492033158. Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting...
New York: Springer, 2021. — 392 p. Today, it is all about the connected smarter world based on the new emerging technologies and Artificial Intelligence (AI) for sustainable development. Sustainable development purposes have been designed for meeting the human development goals while simultaneously sustaining the ability of natural resources in order to maintain the natural...
Springer/Posts & Telecom Press, 2023. — 398 p. Firstly, it takes common competition scenarios as a guide by giving the main processes of using Machine Learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover,...
Boca Raton: CRC Press/Chapman & Hall, 2022. — 149 p. This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small...
IGI Global, 2021. - 351p. - ISBN 9781799854937 Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic...
Packt, 2018. — 172 p. — ISBN: 1789343704. Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
Packt Publishing, 2016. — 252 p. — ISBN: 978-1-78588-295-1. Design efficient machine learning systems that give you more accurate results. Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of...
Wiley-Scrivener, 2024. — 327 p. — (Серия: Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN 978-1-394-23392-2. Метаэвристика для машинного обучения: алгоритмы и приложения The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
O’Reilly Media, Inc. 2023. — 122 p. — (2nd Early Release). — ISBN 978-1-492-09761-7. Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of...
Boca Raton: CRC Press, 2022. — 261 p. Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural...
CRC Press, 2022. — 442 p. — (Chapman & Hall / CRC Data Mining and Knowledge Discovery Series). — ISBN 9781003143376. Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their...
2nd Edition. — Apress Berkeley, 2023. — 661 p. — eBook ISBN: 978-1-4842-9801-5. This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry...
MIT Press, 1990. — 176 p. The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning. Theorems are...
2nd.ed. - The MIT Press, 2020. - 856p. - ISBN 0262044692 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications...
Packt Publishing, 2025. — 512 p. Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Book Description: Written by a seasoned software engineer with...
Syncfusion, 2017. — 114 p. Support Vector Machines (SVMs) are some of the most performant off-the-shelf, supervised machine-learning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. He also includes numerous code examples and a lengthy...
2nd ed. — Springer, 2017. — 348 p. This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees,...
CRC Press, 2022. — 199 p. — ISBN 978-0-367-62256-5. Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed...
Wiley, 2021. — 216 p. — ISBN 978111968236. Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series...
O’Reilly Media, Inc., 2023. — 317 p. — ISBN13 9781098106652. — ISBN10 1098106652. With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using...
John Wiley & Sons, Inc., 2019. — 307 p. — ISBN: 978-1-119-54567-5. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on...
Springer, 2024. — 530 p. — ISBN 978-981-99-3916-9. This book provides a comprehensive and systematic introduction to the principal Machine Learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum...
New York: Springer, 2022. — 283 p. In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data...
Manning Publications, 2023. — 288 p. Make your Deep Learning models more generalized and adaptable! These practical regularization techniques improve training efficiency and help avoid overfitting errors. Regularization in Deep Learning teaches you how to improve your model performance with a toolbox of regularization techniques. It covers both well-established regularization...
ITexLi, 2022. — 156 p. — ISBN 1839684771 9781839684777 1839684739 9781839684739 183968478X 9781839684784. This book discusses the theoretical analysis of active learning and contains practical recommendations for its implementation. In the context of globalization changes in educational systems, it is important to modify approaches to the educational process and introduce...
BPB Publications, 2023. — 380 p. A practical guide to mastering Classification algorithms for Machine Learning. Key Features - Get familiar with all the state-of-the-art classification algorithms for machine learning. - Understand the mathematical foundations behind building machine learning models. - Learn how to apply machine learning models to solve real-world industry...
Dr. Wasim Akram Mandal. — 2025. — 101 p. Artificial intelligence is evolving at an unprecedented pace, and new breakthroughs continue to reshape the way we interact with technology. While OpenAI's ChatGPT has dominated the AI space, a new contender has emerged—DeepSeek AI, an innovative and powerful language model that challenges the status quo. This book, Mastering DeepSeek...
Packt Publishing, 2024. — 176 p. Key Features: Learn how to implement a pipeline for optimal model creation from large datasets and at lower costs Gain profound insights within your data while achieving greater efficiency and speed Apply your knowledge to real-world use cases and solve complex ML problems Book Description: Building accurate machine learning models requires...
World Scientific Publishing Company, 2021. — 296 p. This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of Machine Learning. It consists of three parts. The first is an overview of the history of Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS), and...
Apress, 2024. — 631 p. — ISBN-13 979-8-8688-0601-8. The role of data engineering has become increasingly crucial. Data engineering is the foundation on which organizations build their reporting, analytics, and machine learning capabilities. Data engineering as a discipline transforms raw data into reliable and insightful informational resources that provide valuable insights to...
Wiley, 2024. —480 p. — ISBN 978-1-394-21415-0. Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As...
Leanpub, 2022. — 92 p. Version: 2022-09-10 The book is intended to get you acquainted with the world of Supervised Machine Learning and does not assume previous knowledge of the field. The commonly leveraged Linear Regression technique used to provide predictions that are continuous in nature is detailed in the book. Sample Python code included! If you are not familiar with...
O’Reilly Media, 2022. — 563 p. — ISBN 978-1-098-11292-9. Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved. But first,...
BPB Publications, 2019 — 280 p. — ISBN 9789389328127. Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing....
O’Reilly, 2019. — 362 p. — ISBN: 1492035645. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled...
O’Reilly Media, 2021. — 304 p. — ISBN 1492053279, 9781492053279. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads–a process Kubeflow makes much easier. This practical book shows data scientists, data...
Final Release: O’Reilly Media, 2023. — 191 p. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you. Using Spark as...
New York: O’Reilly, 2019. — 220 p. — ISBN: 9781492039822. Code files only! Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical...
Packt Publishing, 2017. — conv 1410 p. — ASIN B076CRXB76. Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical...
Springer, 2006. — 482 p. Third InternationalWorkshop, MLMI 2006, Bethesda, MD, USA, May 1-4, 2006. Revised Selected Papers. The workshop was organized and sponsored jointly by the US National Institute for Standards and Technology (NIST), three projects supported by the European Commission (Information Society Technologies priority of the sixth Framework Programme)—the AMI and...
Independently published, 2021. — 111 p. — ISBN: 13 9781005466787. Artificial Intelligence (AI) is a branch of computer science that seeks to develop computer systems that are capable of human-like intelligence. You can have Artificial Intelligence that replicates the human mind implemented this way rather than just having a computer system that mimics and the entire human...
USA: IGI Global, 2024. — 557 p. — (Advances in Information Security, Privacy, and Ethics (AISPE) Series). – ISBN 9798369341599. Машинное обучение и криптографические решения для защиты данных и сетевой безопасности As the Editors of this comprehensive reference book, Machine Learning and Cryptographic Solutions for Data Protection and Network Security, we are delighted to...
O’Reilly Media, 2025. — 416 p. — ISBN-13: 978-1-098-14924-6. Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and...
AMKCORP Academics, 2024. — 212 p. — ISBN 978-81-970871-3-4. The book titled “The Science of Machine Learning Part 1” serves as a comprehensive guide for both beginners and experienced practitioners in the field of Machine Learning. Covering a wide range of topics, the book provides a thorough introduction to the fundamentals of Machine Learning, as well as advanced techniques...
2nd edition. — 2015. — 263 p. This is a set of lecture notes for an undergraduate class on metaheuristics. The first version of the notes was written for a course I taught in Spring of 2009. As these are lecture notes for an undergraduate class on the topic, which is unusual, these notes have certain traits. First, they’re informal and contain a number of my own personal biases...
Oxford University Press, 2022. — 400 p. — ISBN 9780192845290. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders...
Packt, 2020. — 246 p. — ISBN: 9781838646486. Build secure private blockchain networks to handle mission-critical security challenges such as denial-of-service attacks, user wallets, and pool mining attacks Key Features Explore blockchain concepts such as cryptography, consensus algorithms, and security assumptions Architect network security for mission-critical decentralized...
Packt Publishing, 2021. — 194 p. — ISBN 978-1800567641. Create better and easy-to-use deep learning models with AutoKeras Key Features Design and implement your own custom machine learning models using the features of AutoKeras Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis Get familiar with advanced concepts as multi-modal,...
Elektor Publication, 2022. — 248 р. — ISBN 978-3-89576-502-5. Most people are increasingly confronted with the applications of Artificial Intelligence (AI). Music or video ratings, navigation systems, shopping advice, etc. are based on methods that can be attributed to this field. The term Artificial Intelligence was coined in 1956 at an international conference known as the...
The MIT Press, 2022. — 325 p. — ISBN 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard Machine Learning (ML) techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely...
Springer, 2023 p. — 353 p. — (Lecture Notes in Energy 44). — ISBN 9783031162480. Машинное обучение и его применение к реагирующим потокам: машинное обучение и сжигание This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of...
Emerald Publishing, 2021. — 213 p. — ISBN 978-1-80043-881-1. Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer...
Independently published, 2023. — 83 p. Machine Learning is a fascinating field that has the potential to revolutionize the way we live and work. However, it can be overwhelming for beginners to understand the complex algorithms and concepts involved. This book is designed to make Machine Learning accessible and easy to understand for anyone who wants to learn. The algorithms...
Boca Raton: CRC Press, 2024. — 210 p. Intel oneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing...
Springer, 2018. — 249 p. This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper...
John Wiley & Sons, 2023. — 322 p. — ISBN 9781119817482. There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning...
Academic Press/Elsevier, 2023. — 404 р. — ISBN 978-0-323-89931-4. Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve....
Ташкент, 2015. — 27 с. Теория обобщающей способности - ТОСАМО является альтернативной для наиболее распространённой на сегодняшний день Статистической теории машинного обучения Владимира Вапника. ТОСАМО с помощью математической теории игр и линейной алгебры отвечает на вопросы, не имевшие ответов в предшествующих теориях: 1. Почему максимальная обучающая способность не не всегда...
AppCoda, 2020. — 330 р. — ISBN 978-9-887535-00-3. Machine learning, now more than ever, plays a pivotal role in almost everything we do in our digital lives. Whether it’s interacting with a virtual assistant like Siri or typing out a message to a friend, machine learning is the technology facilitating those actions. It’s clear that machine learning is here to stay, and as such,...
4th ed. — The MIT Press, 2020. — 712 p. — ISBN 9780262358064. A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new...
2nd edition. — The MIT Press, 2021. — 280 p. — (MIT Press essential knowledge). — ISBN 9780262542524. A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice...
Cham: Springer International Publishing, 2018. — 118 p. — ISBN: 978-3-319-71489-9. This book explores break-through approaches to tackling and mitigating the well-known problems of compiler optimization using design space exploration and machine learning techniques. It demonstrates that not all the optimization passes are suitable for use within an optimization sequence and...
London: BPB Publications, 2023. — 306 p. — ISBN 9355518447. Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers. Key Features Gain a comprehensive understanding of TinyML's core concepts. Learn how to design your own TinyML applications from the ground up. Explore cutting-edge models, hardware, and software...
O’Reilly Media, 2017. — 250 p. — ISIN: 978-1491963043. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of...
Springer, 2020. - 500p. - ISBN: 9783030457778 This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in...
Mercury Learning & Information, 2020. — 261 p. — ISBN: 1683924703 As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several...
Mercury Learning and Information, 2024. — 169 p. — ISBN-13: 978-150152-248-2. This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an...
Springer, 2023. — 415 p. — (Statistics and Computing). — ISBN 3031413369. This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over...
2nd edition. — Morgan & Claypool, 2018. — 207 p. — ISBN: 978-1681733043. Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at...
Academic Press, 2023. — 326 p. — eBook ISBN: 9780128229057. Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple...
Springer, 2021. — 162 p. — ISBN 978-3-030-83212-4. This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting;...
Addison-Wesley Professional, 2019. — 592 р. — (Addison-Wesley Data & Analytics Series). — ISBN13: 978-0-13-484562-3. Полное руководство для начинающих по изучению и созданию систем машинного обучения с использованием Python. Книга "Машинное обучение с Python для всех" поможет вам освоить процессы, шаблоны и стратегии, необходимые для построения эффективных систем обучения, даже...
Springer, 2024. - 280 p. - ISBN 3031394763. This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing...
Institution of Engineering & Technology, 2023. — 258 p. — ISBN 978-1-83953-617-5. Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and...
Addison-Wesley Professional, 2018. — 611 p. — ISBN: 978-0-13-486386-3, 0134863860 Искусственный интеллект - это мощный инструмент в руках современного архитектора, разработчика и аналитика. Облачные технологии - ваш путь к укрощению искусственного интеллекта. Тщательно изучив эту незаменимую книгу от Ноя Гифта, легендарного эксперта по языку Python, вы легко научитесь писать...
The MIT Press, 2020. — 236 p. — ISBN 9780262538701. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through...
Boca Raton: CRC Press, 2022. — 405 p. Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees...
CRC Press, 2023. — 268 p. — ISBN 978-1-032-37426-0. This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of...
Orchid Publishing, 2023. — 431 p. Are you ready to dive into the fascinating world of Machine Learning and Artificial Intelligence? Do you want to understand the technology that powers everything from personalized recommendations to self-driving cars? If so, "Machine Learning With Python Programming : 2023 A Beginners Guide" is the book you've been waiting for. This...
New York: IGI Global, 2017. — 1270 p. Continuous improvements in technological applications have allowed more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but it increases the overall probability of technological progression. The Handbook of Research on Machine Learning Innovations and Trends is a key resource on the...
New York: Springer, 2017. — 231 p. This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the...
Apress, 2018. — 339 p. Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data...
Amazon.com Services LLC, 2020. — 51 p. — ISBN: 979-8633574968. Are you prepared for the inevitable AI revolution? How can you use it in your current role as a business leader? Artificial intelligence has become the center of strategic decision making for organizations. It disrupts the way industries function – from sales and marketing to finance and HR, companies are betting on...
Springer, 2020. — 627 p. This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains,...
Packt Publishing, 2016. — 252 p. — ISBN: 978-1-78588-295-1. Design efficient machine learning systems that give you more accurate results. Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of...
CRC Press, 2023. — 298 p. — ISBN 978-1003002611. Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and...
Wiley-Scrivener, 2024. — 327 p. — (Серия: Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN 978-1-394-23392-2. The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Manning Publications, 2024. — 504 p. — ISBN-13: 978-1633438835. Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization...
Morgan Kaufmann, 1990. — 815. As the field of machine learning enjoys unprecedented growth and attracts many new researchers, there is a need for regular summaries and comprehensive reviews of its progress. This volume is a sequel to the previous volumes of same title: Volume I appeared in 1983, Volume II in 1986. Volume III presents sample of machine learning research...
World Scientific Publishing Europe Ltd, 2024. — 240 p. — ISBN 978-1800615021. Cutting through the mass of technical literature on machine learning and AI and the plethora of fear-mongering books on the rise of killer robots, Secrets of Machine Learning offers a clear-sighted explanation for the informed reader of what this new technology is, what it does, how it works, and why...
BPB Publications, 2022. — 751 p. — ISBN 978-93-91030-42-1. Master the ML process, from pipeline development to model deployment in production. Key Features Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. A step-by-step approach to cover every data science task with utmost efficiency and highest performance. Access...
O’Reilly Media, 2021. — (Early Release). — ISBN 978-1-492-09079-3. Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde–Google Developer Expert in machine learningand the web–provides a...
O’Reilly Media, 2021. — 464 p. — ISBN 978-1-098-10236-4. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn proven ML techniques to solve a variety of image problems, including classification, object detection, autoencordes, image generation,counting,and captioning. This book...
O’Reilly, 2020. — 408 p. — ISBN: 9781098115784. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into...
Springer, 2022. — xxiv, 264 p. — ISBN 978-981-16-9839-2, 978-981-16-9840-8. Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly...
Manning Publications, 2022. — 177 p. Make your deep learning models more generalized and adaptable! These practical regularization techniques improve training efficiency and help avoid overfitting errors. Regularization in Deep Learning teaches you how to improve your model performance with a toolbox of regularization techniques. It covers both well-established regularization...
The MIT Press, 2017. — 272 p. — ISBN: 0262036827. If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning - programming computers to learn from data - has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing,...
Singapore: World Scientific, 2019. — 329 p. This is a comprehensive book on the theories of Artificial Intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these Machine Learning techniques and describes the problem of causality. This...
Singapore: World Scientific, 2021. — 321 p. This is a comprehensive book on the theories of Artificial Intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these Machine Learning techniques and describes the problem of causality. This...
Birmingham: Packt Publishing, 2021. — 311 p. — ISBN 1800567685. Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning Key Features Get up to speed with AutoML using the platform of your choice, such as OSS, Azure, AWS, or GCP . Eliminate mundane tasks in data engineering and reduce human errors in ML...
Apress, 2019. — 384 p. - ISBN: 978-1-4842-3787-8 Code files only! Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only...
Kluwer, 1993. — 341 p. — ISBN: 0-7923-9277-9. One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable...
Sybex, 2019. — 528 p. — ISBN: 1119556716. Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While...
Independently published, 2020. — 234 p. — ASIN B08R2YCNKN. Do you know all the automatic recommendations come when you type something while searching is a case of machine learning. Isn't it magical ? But nothing is, grab this book and Get an insight of what machine learning is and not just that. Learn how to train your own models using different algorithms like regressors,...
CRC Press, LLC 2020. — 183 p. Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document image analysis (DIA) domain using machine learning since it covers multiple document processing problems. Starting with an explanation of how Artificial Intelligence (AI) plays an important role in this domain,...
Springer, 2021. — 170 p. — ISBN 978-3-030-74041-2. This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing...
Springer, 2022. — 54 p. — ISBN 978-3-031-15892-6. This SpringerBrief presents the underlying principles of Machine Learning (ML) and how to deploy various Deep Learning (DL) tools and techniques to tackle and solve certain challenges facing the cybersecurity industry. By implementing innovative Deep Learning solutions, cybersecurity researchers, students and practitioners can...
Apress, 2019. — 347 p. — ISBN: 1484239156. 2nd.ed. (Code files) Harness the power of MatLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MatLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that...
Springer, 2022. — 394 p. — (Communications and Control Engineering). — ISBN 978-3-030-95859-6. This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power...
First Release. — O’Reilly Media, 2022. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure,...
Packt, 2023. — 242 p. Master the art of machine learning by using the one-of-a-kind Qlik platform, and take your data analytics skills to the next level Key Features Gain a solid understanding of machine learning concepts and learn to effectively define a problem Explore the application of machine learning principles within the Qlik platform Apply your knowledge of ML to...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Springer, 2006. — 502 p. Second International Workshop, MLMI 2005, Edinburgh, UK, July 11-13, 2005. Revised Selected Papers. The conference program featured invited talks, full papers (subject to careful peer review, by at least three reviewers), and posters (accepted on the basis of abstracts) covering a wide range of areas related to machine learning applied to multimodal...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Final version! Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
Sanshodhana, 3016. - 39 p. - ISBN: 1520269277 This book is an introduction to theory and experiments in Machine Learning. The book has simple examples to explain what is possible using Machine Learning, this first edition has cut down on the mathematical aspect and concentrates on logical connections in explaining Machine Learning through experiments.
Scrivener Publishing, 2022 — 480 p. — ISBN: 1119821258. The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software...
Manning Publications, 2018. — 256 p. — ISBN: 978-1-6172-9333-7. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
Manning Publications, 2018. — 224 p. — ISBN: 978-1-6172-9333-7. Code files only! Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine...
AI Sciences LLC, 2018. — 184 p. — ASIN B07G4M16JF. Are you thinking of learning more about Machine Learning using Python? This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you...
IGI Global, 2024. — 327 p. — (Advances in Computational Intelligence and Robotics (ACIR) Book Series). — ISBN 979-8-3693-5271-7. In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative...
O’Reilly Media, Inc., 2023. — 325 р. — ISBN-13: 978-1-098-14682-5. Take a data-first and use-case–driven approach with Low-Code AI to understand Machine Learning and Deep Learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn...
Springer, 2021. — 147 p. — (Terrorism, Security, and Computation). — ISBN 3030606139, 9783030606138. This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram’s behavior and reduce attacks...
New York: Morgan & Claypool Publishers, 2014. — 128 p. While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have...
Manning Publications, 2024. — 375 р. — ISBN: 978-1617299025. Practical patterns for scaling Machine Learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Construct machine learning pipelines with data ingestion, distributed...
Scatterplot Press, 2024. — 145 p. — ISBN 9781835461969. Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly. Key Features Navigate through Python's machine learning libraries effectively. Learn exploratory data analysis and data scrubbing techniques. Design and evaluate...
Oxford University Press, 2020. — 272 p. — ISBN: 978-0-19-256309-5. Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and...
Packt Publishing, 2017. — 570 p. — ISBN: 9781785280511. Building Machine Learning applications with R. Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the...
Wiley-Scrivener, 2023. — 273 p. Machine Learning and Deep Learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated...
CRC Press, 2025. — 264 p. — ISBN: 978-1-003-42590-8. This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences....
Packt, 2020. — 311 p. — ISBN: 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment. Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and tricks and gain...
Пер. с англ. — СПб.: БХВ-Петербург, 2024. — 448 с.: ил. — (От профессионалов для профессионалов). — ISBN: 978-5-9775-1903-8. Книга рассказывает о продвинутых приёмах машинного обучения и науки о данных (data science) на основе задач, решаемых на всемирно известной платформе Kaggle. Показано (в том числе на примере увлекательных интервью с Kaggle-гроссмейстерами), как устроена...
Elsevier, 2015. — 84 p.
Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate...
Apress, 2018. - 510p. - ISBN: 978-1484238721 Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book—Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an...
Packt Publishing, 2016. — 1123 р. Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and...
Wiley, 2014. — 408 p. — ISBN: 1118889061. Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML...
Birmingham: Packt Publishing, 2019. - 158 p. - ISBN: 1838550356. Key Features Your handy guide to building machine learning workflows in Go for real-world scenarios. Build predictive models using the popular supervised and unsupervised machine learning techniques. Learn all about deployment strategies and take your ML application from prototype to production ready Book...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Packt Publishing, 2017. — 449 p. — ISBN: 978-1785889622. Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide. About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering...
2nd Edition. — Packt Publishing, 2020. — 798 p. — ISBN: 978-1-83882-029-9. Code files only! Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in...
Packt Publishing, 2018. — 646 p. — ISBN: 978-1-78862-111-3. Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models Explore and master the most important algorithms for solving complex machine learning problems. Machine learning is a subset of AI that aims to make modern-day computer systems smarter and...
Chapman & Hall, 2025. — 344 p. — eBook ISBN 978-1-003-29633-1. This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the...
Springer, 2023. — 241 p. — ISBN 978-3-031-31010-2. This book introduces the concept of “bespoke learning”, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides information about the system’s behaviour/structure, and the aim is to learn the...
O’Reilly Media, Inc., 2024. — 307 р. — ISBN-13: 978-1-098-14654-2. As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader...
Packt Publishing, 2016. — 324 p. — ISBN: 1784394750, 9781784394752. Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through...
O’Reilly Media, Inc., 2024. — 556 p. — ISBN 978-1-098-15601-5. Производственные системы машинного обучения: инженерные модели и конвейеры машинного обучения Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a...
Morgan & Claypool, 2022. — 190 p. — (Synthesis Lectures on Artificial Intelligence and Machine Learning). — ISBN 1636393438. Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of...
Erik Cuevas, Jorge Galvez, Omar Avalos, Fernando Wario. — Wiley-IEEE Press, 2024. — 432 p. — ISBN 978-1394229642. Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches. Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence,...
Springer Singapore, 2023. — 295 p. — (Algorithms for Intelligent Systems) — eBook ISBN: 978-981-99-6645-5. Provides rich set of chapters of machine learning and metaheuristic optimization Emphasizes optimization algorithms such as PSO, ant colony optimization, cuckoo search algorithm, etc. Includes real-world examples with attention to theoretical aspects for better...
New York: Springer, 2018. — 286 p. This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides...
Apress, 2024. — 478 p. — ISBN 9798868803536, ASIN B0CYNRV8BK. This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various...
Oxford: Claredon Press, 1994. — 488 p. The present volume records the Machine Intelligence Workshop of 1992, held at Strathclyde University's Ross Priory retreat on Loch Lomond, Scotland. Here the series entered not only its second quarter-century but a new phase. As can be seen in these pages, machine learning emerged to declare itself as a seed-bed of new theory, as a...
Routledge, 2023. — 181 p. — ISBN 9781032362427. Machine Learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with Machine Learning (ML) are better placed to navigate the increasingly digital world we live in. There is a view that Machine Learning is a highly technical subject that can only be...
Springer, 2023. — 320 p. — ISBN 978-3-031-19638-6. Поддержка машинного обучения для диагностики неисправностей систем-на-чипе This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after...
IGI Global, 2021. - 388p. - ISBN 9781799836247 Traditional machining has many limitations in today’s technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing...
CRC Press, 2021. — 402 p. — ISBN 978-0-367-55418-7; ISBN: 978-0-367-55419-4 ; ISBN: 978-1-003-09345-9. Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text...
Packt Publishing, 2019. — 448 p. — ISBN: 978-1-78934-634-3. Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and...
New York: Springer, 2007. — 738 p. This book represents a comprehensive introduction into both conceptual and rigorous brain and cognition modelling. It is devoted to understanding, prediction and control of the fundamental mechanisms of brain functioning. The reader will be provided with a scientific tool enabling him or her to perform a competitive research in brain and...
Packt Publishing, 2018. — 254 p. — ISBN: 978-1789806199. Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book DescriptionMachine...
Birmingham: Packt Publishing, 2022. — 442 p. — ISBN 1801813574. Learn the principles of quantum machine learning and how to apply them in finance. Key Features Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods. Use methods of analogue and digital quantum computing to build powerful generative models. Create...
CRC Press, 2023. — 425 p. — (Smart Technologies for Engineers and Scientists). — ISBN 978-1-032-47929-3. Применение математического моделирования, машинного обучения и интеллектуальных вычислений для промышленного развития. The text focuses on mathematical modeling and applications of advanced techniques of Machine Learning, and Artificial Intelligence, including artificial...
Springer, 2011. — 370 p. Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition,...
Springer, 2022. — 136 p. — (Synthesis Lectures on Learning, Networks, and Algorithms). — ISBN 3031190661. This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of...
Springer, 2023. — 306 p. — ISBN 978-981-99-0392-4. This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Packt Publishing, 2022. — 382 p. Key Features: Understand the need for high-performance computing (HPC) Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker Learn best practices and architectures for implementing ML at scale using HPC This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then...
Birmingham: Packt Publishing, 2022. — 257 p. — ISBN 180324836X. Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming. Key Features Work on streaming use cases that are not taught in most data science courses. Gain experience with state-of-the-art tools for streaming data. Mitigate various challenges while...
Springer, 2024. — 287 p. — (Machine Intelligence for Materials Science). — ISBN 978-3-031-44621-4. Focusing on the fundamentals of Machine Learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced Machine Learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical...
Boca Raton: CRC Press, 2025. — 308 p. Symbolic Regression (SR) is one of the most powerful Machine Learning (ML) techniques that produces transparent models, searching the space of mathematical expressions for a model that represents the relationship between the predictors and the dependent variable without the need of taking assumptions about the model structure. Currently,...
Wiley-Scrivener, 2023. — 239 p. This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial Intelligence (AI) and Machine Learning (ML)...
Springer, 2021. — 349 p. — ISBN 9789811594915. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these...
Wiley-Scrivener, 2024. — 385 p. This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using Machine Learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of...
CRC Press, 2018. — 349 p. — ISBN: 978-0-8153-5622-6. Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial...
John Wiley & Sons, Inc., 2019. — 307 p. — ISBN: 978-1-119-54563-7. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on...
Springer, 2022. — 109 p. — (Wireless Networks). — ISBN 978-3-031-16374-6. This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing...
Springer, 2024. — 351 p. — ISBN 978-3-031-50713-7. The book systematically expounds the main results obtained by the author in the field of design and optimization of high-speed integrated circuits (ICs) and their standard blocks (heterogeneous ICs, analog-to-digital and digital-to-analog converters, input/output cells, etc.) operating in non-standard conditions (deviations of...
New York: Morgan & Claypool, 2018. — 172 p. Covariance Matrices and Applications Data Representation by Covariance Matrices Covariance Matrices for Data Representation Statistical Interpretation Geometry of SPD Matrices Euclidean Distance Interpretations and Motivations for the Different Invariances Basic Riemannian Geometry Affine-invariant Riemannian Metric on SPD Matrices...
Independently published, 2021. — 93 p. — ASIN B0952D2T8C. The Machine Learning process is revolutionizing many sectors in engineering including manufacturing sectors such as the Friction Stir Welding Process. The implementation of machine learning algorithms in Friction Stir Welding process not only reduced the time of the experiment but also reduces the cost of the experiment....
CRC Press, 2023. — 379 р. — ISBN: 978-1-032-07452-8. The utilization of Machine Learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in...
2nd Edition. — The MIT Press, 2019. — 504 р. — (Adaptive Computation and Machine Learning series). — ISBN: 978-0262039406. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It...
Amazon Digital Services LLC, 2019. — 175 p. Python with machine learning is increasing day-to-day popularity. Python is currently a widely used programming language for machine learning. Like the use of python in professional work, it is also growing reputation in the academic sector. In Coursera, various online courses, like programming language for machine learning, the use...
Manning Publications Co, 2021. — 493 p. — ISBN 9781617295645. Code Files Only! At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed...
Independently published, 2024. — 84 p. Do you find yourself unsure of how to apply your existing knowledge to Python? If you are a beginner programmer who wants to learn Python Machine Learning, this book is for you. This book will help you understand how to use Python to apply your existing skills to Machine Learning problems. Machine Learning is a fascinating and powerful...
Heidelberg: O’Reilly, 2018. — 183 S. — ISBN: 978-3-96009-052-6. Language: German Machine Learning erreicht beinahe alle Bereiche der Technik und der Gesellschaft. In diesem Buch bekommen Sie die schnellstmögliche Einführung in das äußerst umfangreiche Themengebiet des Machine Learning und der statistischen Datenanalyse. Dabei werden alle wesentlichen Themen abgedeckt und mit...
Springer, 2023. — 312 p. This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions?Chebyshev, Legendre, Gegenbauer, and...
Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao. — Springer, 2022. — 464 p. — ISBN 978-3-030-96755-0/ This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution...
Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias, Pavan Turaga, Raja Ayyanar, Devarajan Srinivasan. — Morgan & Claypool Publishers, 2020. — 91 p. — ISBN: 978-1681739076. The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance....
2nd Edition. — World Scientific Publishing Co. Pte. Ltd., 2019. — 302 p. — ISBN 978-981-120-195-0. Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition) (Series in Machine Perception and Artificial Intelligence) 2nd Edition This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories,...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
Nova Science Pub Inc., 2021. — 248 p. — ISBN 978-1536187571, 1536187577. Support Vector Machines: Evolution and Applications reviews the basics of Support Vector Machines (SVM), their evolution and applications in diverse fields. SVM is an efficient supervised learning approach popularly used for pattern recognition, medical image classification, face recognition and various...
Springer, 2022. — 128 p. This book discusses the Versatile Video Coding (VVC), the ISO and ITU state-of-the-art video coding standard. VVC reaches a compression efficiency significantly higher than its predecessor standard (HEVC) and it has a high versatility for efficient use in a broad range of applications and different types of video content, including Ultra-High Definition...
2nd Edition. — Wiley, 2023. — 611 p. — ISBN 978-1119903831. An up-to-date introduction to a market-leading platform for data analysis and machine learning. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to...
2nd Edition. — Balige Publishing, 2023. — 355 p. Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it offers a wide range of...
New York: Springer, 2022. — 343 p. This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions...
2nd edition. — CRC Press, 2023. — 866 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN 978-1-003-26487-3. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic...
Leanpub, Giannis Tolios, 2022. — 107 p. Упрощение машинного обучения с помощью PyCaret: подход с минимальным кодом для начинающих и экспертов! A beginner-friendly introduction to Machine Learning (ML) with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of Artificial Intelligence (AI), by easily training and deploying...
Packt, 2019. — 426 p. — ISBN: 1789534143. Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language...
CRC Press, 2022. — 261 p. — ISBN 978-1-032-07159-6. Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine,...
Coolbullet Publishing, 2018. — 61 p. — ASIN B07JB516BF. This book will describe step by step, how to take a dataset and create a machine learning model and deploy this to a web application. Do you want to start building Machine Learning Models without having to wade through lots of theoretical equation dense, lengthy textbooks? Then read this book. This book is a practical text,...
Wiley-IEEE Press, 2025. — 384 p. — ISBN-13: 978-1394294954. A practical guide to AI applications for Simple Python and MatLAB scripts Machine Learning and AI with Simple Python and MatLAB Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular...
Berlin: Walter de Gruyter, 2024. — 210 p. — (De Gruyter Textbook). — ISBN 3111288471. This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms,...
Manning Publications, 2023. — 374 p. Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Unsupervised Learning with Generative AI you’ll learn: Fundamental building blocks and concepts of Machine Learning and unsupervised learning Data...
2nd Edition. — Packt Publishing Ltd, 2018. — 468 p. — ISBN: 978-1-78847-624-9. A beginner's guide to learning 3D game development with C++ and UE4. Learning to program in C++ requires some serious motivation. Unreal Engine 4 (UE4) is a powerful C++ engine with a full range of features used to create top-notch, exciting games by AAA studios, making it the fun way to dive into...
Routledge, 2023. — 219 p. Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the...
Springer, 2025. — 548 p. — ISBN 978-981-97-5332-1. Conducting an in-depth analysis of Machine Learning, this book proposes three perspectives for studying Machine Learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the...
Springer, 2019. — 314 p. This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is...
Springer, 2022. — 245 p. — ISBN 978-981-16-8503-3. CRISPR: обзор литературы, созданной машинами Clustered regularly interspaced short palindromic repeats (CRISPR) is a continuously evolving technology, with the first report of a specific sequence in Escherichia coli published in 1987. In 2007, CRISPR was considered a bacterial immune system used to resist virus invasion. It was...
Автор, 2023. — 65 с. — ISBN 978-5-0060-1962-1. Краткий гайд для новичков по машинному и глубокому обучению с разбором кода. Здесь вы найдете необходимый минимум по предмету, истолкованный языком, понятным школьнику. Некоторые разделы написаны с помощью chatGPT. По прочтении вы избавитесь от страха перед технологией и освоите базовый инструментарий подготовки данных, их загрузке...
2018. — 75 p. — ASIN B07FKZN93N. How can a beginner approach machine learning with Python from scratch? Why exactly is machine learning such a hot topic right now in the business world? Ahmed Ph. Abbasi will lead you from being a complete beginner in learning a sound method of data analysis that uses algorithms, which learn from data and produce actionable and valuable...
Teaching handbook. — Ekaterinburg: Ural University Press, 2024. — 84 p. The book introduces the fundamental principles of various aspects of data analysis, providing students with a foundation in preparing them for further tasks using tools such as Python for machine learning. The main data analysis tools are considered: SQL, Python, Power BI and the Git version control system....
AI Publishing, 2020. — 308 p. — ISBN B08QJMNVCX. 10 Machine Learning Projects Explained from Scratch Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful...
O’Reilly, 2020. — 260 p. — ISBN: 978-1-492-04510-6. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn...
Packt, 2020. — 384 p. — ISBN: 9781838826048. Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the art of data-driven problem-solving with hands-on...
New York: Springer, 2021. — 181 p. The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time...
2nd Edition. — Wiley, 2020. — 420 p. — ISBN: 978-1-119-64225-1. Dig deep into the data with a hands-on guide to machine learning with updated examples and more! This book provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant,...
McGraw-Hill LLC, 2025. — 249 p. This practical guide shows, step by step, how to use Machine Learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of...
Birmingham: Packt Publishing, 2022. — 305 p. — ISBN 1803246154. Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems. Key Features Explore various explainability methods for designing robust and scalable explainable ML systems. Use XAI frameworks such as LIME and SHAP to...
Springer, 2021. — 182 p. — (SpringerBriefs in Petroleum Geoscience & Engineering). — ISBN 978-3-030-71767-4. This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Arcler Press, 2021-12-01. — 264 p. — ISBN: 978-1-77469-233-2. Deep-learning and machine-learning have gained a significant importance in the last few years. New inventions and discoveries are taking place every day to exploit the concepts of machine-learning technique. The aim of this book is to present the fundamentals of machine-learning with an emphasis on deep-learning,...
2nd ed. — Packt Publishing, 2018. — 552 p. — ISBN: 1789347998. An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions...
Busharest: GAER Publishing House, 2020. — 76 p. In Machine Learning (ML), support-vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. This book covers in the first part the theoretical aspects of support vector machines and their functionality, and then based on the discussed...
Manning. 2016. 427 p. Eng. eal-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also...
Andriy Burkov, 2019. — 160 p. — ISBN: 978-1-9995795-0-0. Update 2019-07-05 Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful...
Andriy Burkov, 2019. — 160 p. — ISBN: 978-1-9995795-0-0. Update 2019-07-05 Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful...
Globaltech NTC, 2018. — 144 р. You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been...
O’Reilly Media, 2022. — 333 p. — ISBN-13: 978-1-492-08992-6. Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train...
Chapman and Hall/CRC, 2022. — 154 p. — ISBN 9781003267003. This book introduces the reader to Artificial Intelligence and its importance to our future. Campbell uses behavioural psychology, explores technology, economics, real-life and historical examples to predict five future scenarios with AI. Illustrating through speculative fiction, she describes possible futures after AI...
Mercury Learning & Information, 2020. — 363 p. — ISBN 978-1-68392-495-1. Code Files Only! This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth...
Packt, 2021 — 344 p. — ISBN 1801076804, 9781801076807. Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key Features Get well-versed with DataRobot features using real-world examples Use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle Make use of...
Bentham Science Publishers, 2022. — 360 p. — ISBN 978-1-68108-940-9. Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include...
CRC Press, 2023. — 339 р. — ISBN: 978-0367569129. Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various...
Монография На английском языке 2021, 244 с. Many Machine Learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for...
CRC Press, 2024. — 249 p. Data Science and Machine Learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable...
CRC, 2020. — 430 p. — ISBN: 9781466510845. Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the...
O’Reilly Media, 2016. — 290 р. — ISBN: 978-1491964606. In Practical Machine Learning with H2O, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H2O, import and export data, and distinguish H2O algorithms. Readers will then...
Ciml. info; Published by TODO, 2015. — 227 p. This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a...
BPB Publications, 2023. — 150 р. — ISBN-13: 978-93-55511-133. A guide to understand the basics of Machine Learning and its application in the field of education. Key Features: - Create a more efficient and effective learning environment that meets the needs of all students. - Learn how to use the profound Machine learning advancements in the field of education. - Understand how...
Addison-Wesley Professional, 2019. — 592 р. — (Addison-Wesley Data & Analytics Series). — ISBN13: 978-0-13-484562-3. Полное руководство для начинающих по изучению и созданию систем машинного обучения с использованием Python. Книга "Машинное обучение с Python для всех" поможет вам освоить процессы, шаблоны и стратегии, необходимые для построения эффективных систем обучения, даже...
Wiley, 2022. — 53 p. In this book, we focus on the commercial applications of graph analysis and graph-enhanced machine learning (ML), which takes the form of graph data science. We also use Neo4j graph technology to illustrate a graph data science platform. You take a quick look at graph data science and its uses before covering the journey of graph data science adoption. You...
Boca Raton: CRC Press, 2024. — 223 p. Machine Learning (ML) has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response....
Addison-Wesley Professional, 2018. — 611 p. — ISBN13: 978-0-13-486386-3, 0134863860 Искусственный интеллект - это мощный инструмент в руках современного архитектора, разработчика и аналитика. Облачные технологии - ваш путь к укрощению искусственного интеллекта. Тщательно изучив эту незаменимую книгу от Ноя Гифта, легендарного эксперта по языку Python, вы легко научитесь писать...
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
O’Reilly, 2020. - 130p. - ISBN: ISBN: 9781492050124 If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data...
Springer, 2018. — 726 p. This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things,...
Springer, 2012. — 602 p. — ISBN10: 3642353258, ISBN13: 978-3642353253. This book constitutes the refereed proceedings of the Second International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2014, held in Cairo, Egypt, in November 2014. The 49 full papers presented were carefully reviewed and selected from 101 initial submissions. The papers are...
Independently published, 2025. — 100 p. Unlock the Power of Machine Learning with This Beginner-Friendly Guide! Are you ready to dive into the exciting world of Machine Learning but don’t know where to start? "Machine Learning for Beginners: A Step-by-Step Guide with Code Examples" is your perfect starting point! This book is designed for absolute beginners who are eager to...
Springer, 2023. — 204 p. This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning...
Cham: Springer, 2023. — 801 p. This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for...
Springer, 2023. — 319 p. — (Artificial Intelligence: Foundations, Theory, and Algorithms). Machine Learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it...
Springer, 2012. — 287 p.
This monograph presents key method to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted,...
Birmingham: Packt Publishing, 2022. - 335 p. - ISBN 1803249900. Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use. Key Features Learn best practices about bringing your models to production. Explore the tools available for...
Packt Publishing, 2019. — 456 p. — ISBN: 978-1-78913-636-4. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms...
Birmingham: Packt Publishing, 2022. — 280 p. — ISBN 1803230061. Learn how to leverage feature stores to make the most of your machine learning models Key Features Understand the significance of feature stores in the ML life cycle. Discover how features can be shared, discovered, and re-used. Learn to make features available for online models during inference. Feature store is...
Independently published, 2022. — 564 p. The goal of artificial intelligence is to program computer systems to make use of example information or past experience to address an offered trouble. Numerous successful applications of machine learning exist already, consisting of systems that evaluate past sales data to forecast consumer habits, enhance robotic habits so that a task...
HiTeX Press;, 2024. — 403 p. — ASIN: B0DL4V4L2G. "Few-Shot Machine Learning: Doing More with Less Data" is an illuminating exploration into the cutting-edge techniques that enable machines to learn efficiently from limited data. This book delves deep into the domain of few-shot learning—a revolutionary approach that challenges the traditional dependency on vast datasets. By...
Birmingham: Packt Publishing, 2022. - 551 p. - ISBN 1803246804. Build real-world time series forecasting systems which scale to millions of time series by mastering and applying modern concepts in machine learning and deep learning. Key Features Explore industry-tested machine learning techniques to forecast millions of time series. Get started with the revolutionary paradigm...
Packt Publishing, 2016. — 258 p. — ISBN: 978-1-78439-658-9. Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Apress, 2018. — 355 p. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning...
O’Reilly Media, 2017. — 237 р. — ISBN: 978-1-491-92413-6. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
O’Reilly Media, 2017. — 237 р. — ISBN: 978-1-491-92413-6. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
New York: Springer, 2022. — 260 p. This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice...
Packt Publishing, 2019. — 294 р. — ISBN: 978-1788830577. Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right...
Jamba Academy, 2023. — 623 p. Are you ready to dive into the world of Python Machine Learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of Machine Learning and the powerful Scikit-learn library. Key...
New York: Wiley-Scrivener, 2022. — 416 p. The book focuses on the way that human beings and computers interact to ever increasing levels of both complexity and simplicity. Assuming very little knowledge, the book provides content on theory, cognition, design, evaluation, and user diversity. It aims to explain the underlying causes of the cognitive, social and organizational...
New York: Springer, 2020. — 591 p. This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of Machine Learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on Machine...
Packt Publishing, 2021. — 249 p. — ISBN 978-1-80056-079-6. Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow Use MLflow to iteratively develop a ML model and manage it Discover and work with the...
Second Edition. — Independently published, 2021. — 281 p. — ISBN 978-1838132217. This Second Edition improves clarity of code, general formatting, text explanations as well as updating the AI for 2021 stock selection. Build Your Own AI Investor Breaks down Value Investing for the AI revolution, whilst being accessible to anyone, even if you've never invested in stocks or coded...
Springer, 2020. — 285 p. — ISBN: 978-981-15-2910-8 (eBook). This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is...
Packt Publishing, 2017. — 344 p. — ISBN: 978-1783553112. +Sample Files Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book...
Springer, 2020. — 201 p. — ISBN: 9811586098. This book focuses on both theory and applications in the broad areas of computational intelligence and machine learning. The proceedings of the Seventh International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019) present research papers in the areas of advanced computing, networking, and informatics. It...
Springer, 2024. — 259 p. — ISBN 978-3-031-56430-7. This book is for individuals with a scientific background who aspire to apply Machine Learning (ML) within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous...
Springer Cham, 2025. — 132 p. — (Synthesis Lectures on Engineering, Science, and Technology). — eBook ISBN 978-3-031-82010-6. Comprehensive guide to MLOps. Covers all aspects of the MLOps process. No prior knowledge of MLOps is required. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable...
Cham: Springer International Publishing, 2019. — 206 p. — ISBN: 978-3-319-99223-5. This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational...
Amazon Digital Services LLC, 2019. — 46 p. Order Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science, NumPy, Scikit Learn, Pandas and Tensorflow now to learn all the basic concepts you need to know about machine learning and Python. The purpose of...
Springer Cham, 2022. — 465 p. — (International Series in Operations Research & Management Science, volume 334). — eBook ISBN 978-3-031-16990-8. This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward...
O’Reilly, 2017. — 388 p. — ISBN: 978-1-491-91721-3. Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the...
Springer, 2022. — 390 p. — (Power Systems). — ISBN 978-3-030-77695-4. This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction,...
Packt Publishing, 2018. - 378p. - ISBN: 1788838297 Leverage the power of Apple's Core ML to create smart iOS apps Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple, with APIs...
Elsevier, 2023. — 448 p. — ISBN 978-0443187649. Applications of Artificial Intelligence in Mining, Geotechnical and Geoengineering provides recent advances in mining, geotechnical and geoengineering, as well as applications of artificial intelligence in these areas. It serves as the first book on applications of artificial intelligence in mining, geotechnical and...
1st Edition: arXiv:1909.11512v1, 2019. — 156 p. Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer...
Independently publ, Norton D., 2023. — 196 p. Embark on a transformative journey into the realm of Artificial Intelligence with Machine Learning Mastery. This meticulously crafted hardcover, audiobook, paperback, and e-book compilation is your key to unlocking the profound capabilities of Machine Learning. From foundational theories to real world applications, this guide is...
Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango. — Chapman and Hall/CRC, 2025. — 192 р. — ISBN 978-87-7004-713-5/ The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct...
Apress, 2019. — 347 p. — ISBN: 1484239156. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MatLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses...
Springer, 2024. — 355 р. — ISBN 978-3-031-66841-8. This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of...
Springer, 2008. — 318 p. 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007. Revised Selected Papers. This book contains a selection of revised papers from the 4th Workshop on Machine Learning for Multimodal Interaction (MLMI 2007), which took place in Brno, Czech Republic, during June 28–30, 2007. As in the previous editions of the MLMI series, the...
CRC Press, 2024. — 225 p. This book brings together a blend of different areas of Machine Learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the...
Packt Publishing, 2021. — 270 p. — ISBN 978-1800567887. Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models...
Springer, 2022. — 495 p. This book organizes principles and methods of signal processing and Machine Learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor...
Birmingham: Packt Publishing, 2019. — 771 p. Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully...
Self-published - 2021 - 159 p - ASIN : B09BFBJLDK Is it accurate to say that you are interested about machine learning and AI and you don't have the foggiest idea where to begin? Have you at any point heard individuals discussing Machine Learning however you just have a dubious thought of the real significance? Would you like to see how machine learning could improve on your...
CreateSpace Independent Publishing Platform, 2017. — 56 p. The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple.From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept that...
Springer, 2024. — 265 p. This volume is focused on the review of recent algorithmic and mathematical advances and the development of new research directions for Mathematical Model Approximations via RAMSES (Reduced order models, Approximation theory, Machine learning, Surrogates, Emulators, Simulators) in the setting of parametrized partial differential equations also with...
O’Reilly Media, 2025. — 416 p. — ISBN-13: 978-1-098-14924-6. Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and...
Boca Raton: CRC Press, 2025. — 367 p. This book discusses state-of-the-art reviews of the existing Machine Learning techniques and algorithms including hybridizations and optimizations. It covers applications of Machine Learning via Artificial Intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition...
Apress, 2018. — 642 p. — ISBN: 978-1-4842-3206-4. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner....
CRC Press, 2025. — 222 p. — (Data Science Series). Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and Machine Learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An...
Springer, 2019. — 320 p. — ISBN: 978-3-319-89802-5. The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many...
Springer, 2024. — 315 p. — (Transactions on Computer Systems and Networks). — ISBN 978-981-97-1899-3. This book provides a comprehensive coverage of Machine Learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning...
2nd Edition. — Balige Publishing, 2023. — 585 p. Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using Machine Learning and Deep Learning techniques, all implemented with the power of Python and a...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
Packt Publishing Ltd., 2020. — 404 p. — ISBN: 978-1-78995-608-5. Code files only! Use the power of deep learning with Python to build and deploy intelligent web applications When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you’ll cover the latest tools and technological practices that are being used to implement deep...
1991 — 90 p.
На англ. языке.
The game of go is an ideal problem domain for exploring machine learning: it is easy to define and there are many human experts, yet existing programs have failed to emulate their level of play to date. Existing literature on go playing programs and applications of machine learning to games are surveyed. An error function based on a database of...
New York: Information Science Reference, 2018. — 217 p. Teachers use e-learning systems to develop course notes and web-based activities to communicate with learners on one side and monitor and classify their progress on the other. Learners use it for learning, communication, and collaboration. Adaptive e-learning systems often employ learner models, and the behavior of an...
K.Taylor, 2017. — 413 p. Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases....
FreePress, 2020. — 490 p. Learn how to trade for a living with solid risk-management strategies. Use techniques based on trader psychology for your investment in options and forex. Earn extra income, create your source of passive income and get your own financial freedom.
Scatterplot Press, 2017. — 155 p. — ISBN: 1549617214. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey...
New York: Springer, 2019. — 548 p. This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary;...
CRC Press, 2025. — 264 p. This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to...
Apress, 2019. — 637 p. — ISBN: 1484253159. Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data...
Independently published, 2019. — 129 p. — ISBN: 978-1075281518. Do you want to learn about machine learning? Are you feeling left out in the rat race where everyone is brushing up on their knowledge in the fields of AI and machine learning? Are you on a time crunch and don’t have enough time or resources to take a formal course on machine learning? If the answer to any of the...
Elsevier, 2023. — 254 p. – ISBN 978-0-323-96126-4. Машинное обучение для транспортных исследований и приложений Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in Machine Learning provide new methods to tackle challenging...
Manning Publications Co., 2021. — 273 p. Description the content of the file: In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using...
Boston, MA : Springer-Verlag US, 2009. — 367 p. — ISBN: 978-0-387-88735-7. Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems turns out to be a fertile ground where many tasks can be formulated as learning problems...
Пер. с англ. С. Черников. — СПб.: Питер, 2023. — 256 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1773-4. Освойте ключевые навыки проектирования, разработки и развертывания приложений на базе машинного обучения (МО)! Пошаговое руководство по созданию МО-приложений с упором на практику: для специалистов по обработке данных, разработчиков программного обеспечения и...
М.: МЦНМО, 2014. — 304 с. — ISBN 978-5-4439-2014-6. Книга предназначена для первоначального знакомства с математическими основами современной теории машинного обучения (Machine learning) и теории игр с предсказаниями. В первой части излагаются основы статистической теории машинного обучения, рассматриваются задачи классификации и регрессии с опорными векторами, теория обобщения...
Без издательства, 2024. — 55 с. Прочитав книгу, вы познакомитесь с богатым набором примеров, начиная от простых классификаторов и заканчивая продвинутыми проектами, такими как создание систем рекомендаций и анализ данных. Особое внимание уделяется вопросам интерпретируемости моделей, тестирования и совершенствования решений для повышения их производительности. Кроме того, книга...
Автор, 2023. — 65 с. — ISBN 978-5-0060-1962-1. Краткий гайд для новичков по машинному и глубокому обучению с разбором кода. Здесь вы найдете необходимый минимум по предмету, истолкованный языком, понятным школьнику. Некоторые разделы написаны с помощью chatGPT. По прочтении вы избавитесь от страха перед технологией и освоите базовый инструментарий подготовки данных, их загрузке...
Монография. — Москва: Российский государственный аграрный университет — МСХА имени К. А. Тимирязева, 2024. — 86 с. В монографическом исследовании рассматриваются проблемы создания и моделирования целесообразного поведения интеллектуальных систем, способных целенаправленно функционировать в недоопределенных проблемных средах. Коллективная научная работа объединила результаты...
Boca Raton: CRC Press, 2024. — 377 p. While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in...
Springer, 2020. — 506 p. — ISBN: 978-3-030-40344-7 (eBook). This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced...
Springer, 2018. — 759 p. — ISBN: 978-3-319-73531-3 (eBook). Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: – Basic algorithms:...
Apress Media LLC., 2020. — 177 p. — ISBN13: (electronic): 978-1-4842-6579-6. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what...
AI Publishing LLC, 2020. — 310 p. — ISBN B08LSLHBR8. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce,...
Birmingham: Packt Publishing, 2022. — 395 p. — ISBN 1801074526. Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai Key Features Learn how to train the best models with a single click using H2O AutoML. Get a simple explanation of model performance using H2O Explainability. Easily deploy your trained models to...
Packt, 2022. — 408 p. — ISBN 1801811911, 9781801811910. Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key Features Learn the applications of machine learning in biotechnology and life science sectors Discover exciting real-world applications of deep learning and natural language...
IGI Global, 2024. —308 p. — eBook ISBN 9798369338841. Organizations worldwide grapple with the complexities of incorporating machine learning into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business...
Orange Education Pvt Ltd, AVA, 2025. — 511 p. — ISBN 978-93-48107-13-8. true Preface Artificial Intelligence (AI) has revolutionized the way we interact with technology, shaping innovations across industries and redefining what machines are capable of achieving. From its theoretical roots to practical applications, AI continues to be a field that sparks curiosity and drives...
Ojula Technology Innovations, 2923. — 94 p. Learning algorithms and data structures from this book will help you become a better programmer. Algorithms and data structures will make you think more logically. Furthermore, they can help you design better systems for storing and processing data. They also serve as a tool for optimization and problem-solving. As a result, the...
BPB Publications, 2024. — 392 p. — ISBN 978-93-55519-818. Build high-impact ML/AI solutions by optimizing each step Key Features Build and fine-tune models for maximum performance. Practical tips to make your own state-of-the-art AI/ML models. ML/AI problem solving tips with multiple case studies to tackle real-world challenges. Description This book approaches data science...
Packt Publishing, 2019. — 375 p. — ISBN: 978-1-78934-827-9. Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster...
Packt Publishing, 2017. — 270 p. Your one-stop guide to becoming a Machine Learning expert. Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by...
O’Reilly, 2018. — 226 p. — ISBN: 1491976446. Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, youll first start to learn with regression modelling and then move into more advanced...
Globaltech NTC, 2018. — 144 р. You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been...
Packt, 2020. — 256 p. — ISBN: 9781789801781. !Code files only Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular...
Amazon.com Services LLC., 2020. — 151 p. Become the master of machine learning with this powerful guide. Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin? Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple,...
Birmingham: Packt Publishing, 2023. — 679 p. — ISBN 1804613835. Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guide. Key Features Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites. Learn the process of...
Packt Publishing, 2016. — 444 p. — ISBN: 978-1-78439-475-2. Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the...
Singapore: Springer, 2023. — 119 p. Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular...
Packt Publishing, 2020. — 348 p. — ISBN: 978-1-83882-441-9. Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book,...
Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad (Editor). — Wiley, 2020. — 535 p. — ISBN: 978-1119654742. Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online...
Hoboken: Wiley, 2022. — 482 p. Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes...
De Gruyter, 2024. — 316 p. — (De Gruyter Handbooks of Digital Transformation 1). The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to Artificial Intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on...
Morgan Kaufmann, 2018. — 569 p. — ISBN 9780081006597. Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the...
Springer, 2024. — 179 p. — (Machine Learning: Foundations, Methodologies, and Applications). — ISBN 9819706874. Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and...
BPB Publications, 2025. — 346 р. — ISBN: 978-93-65891-997. Description This book prepares you with the knowledge and skills to confidently excel in the exciting world of machine learning (ML) interviews and launch a successful career in this dynamic field. This book offers a collection of curated questions and answers to help readers understand key ML concepts, including data...
Wien: Springer, 2002. — 344 p. The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is...
Birmingham: Packt Publishing, 2019. — 199 p. — ISBN: 1789345804. Python code files for the book ! Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to...
O’Reilly Media, 2020. — 168 p. — (Early Release). Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools...
Packt Publishing, 2016. — 278 p. — ISBN: 978-1-78439-863-7. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum...
2018. — 246 p. — ISBN: 1727338944. Have you ever wanted to learn how to better use your data? Are you interested in the works of machine learning? If you answered yes to these questions, then this book is for you. Machine Learning and Deep learning are powerful data tools that can help improve businesses. In this book, you will learn: Neural networks Machine learning Python and...
Manning, 2019. — 288 p. — ISBN: 1617295833. !Code files Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Machine Learning for Business teaches you how to make your company more automated, productive, and competitive by mastering practical, implementable machine learning techniques and tools. Thanks to the...
Packt Publishing, 2016. — 298 p. — ISBN: 978-1-78588-660-7. Explore the web and make smarter predictions using Python Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps...
The Guilford Press, 2023. — 435 р. — ISBN 978-1462552924. Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data...
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight from volumes of...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
Read by author. — Udemy, updated 12/2018. — MP4, video: AVC 1280x720, audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN 978-1-492-08525-6. • 2021-12-07: First Release Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning...
IGI Global, 2020. — 314 p. — ISBN 9781799801832. Machine Learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and...
Now Foundations and Trends, 2024. — 186 p. — ISBN 978-1-63828-317-1. Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of...
Bodenseo, 2021. — 453 p. Not only in Machine Learning but also in general life, especially business life, you will hear questiones like "How accurate is your product?" or "How precise is your machine?". When people get replies like "This is the most accurate product in its field!" or "This machine has the highest imaginable precision!", they feel fomforted by both answers....
Apress, 2021. — 296 p. — ISBN 978-1-4842-7149-0. Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a...
Springer, 2018. — 317 p. — ISBN: 978-3319730394. This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General...
Boca Raton: CRC Press, 2024. — 363 p. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by...
New York: Springer, 2020. — 202 p. This book discusses one of the major applications of Artificial Iintelligence (AI): the use of Machine Learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also...
Oxford University Press, 2021. — 333 p. — ISBN 9780190941666. Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data science that requires only a few weeks to learn instead of years of training Though the concept of computers learning to solve a problem may still conjure thoughts of futuristic...
3rd edition. — Packt Publishing, 2020. — 527 p. — ISBN 9781800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data scientists...
2nd Edition. — Chapman and Hall/CRC, 2015. — 452 p. — ISBN13: 978-1-4665-8333-7. Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimization and Search....
Springer, 2018. — 373 p. — ISBN: 3319949888. This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental...
Petaluma US : Roundtree Press, 2018. — 104 p. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence revolution, with AI enhancing existing...
Springer, 2020. - 286 p. - (Algorithms for Intelligent Systems). - ISBN: 9813299894. This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines,...
Scrivener Publishing, 2021. — 528 p. — ISBN 978-1-119-78580-4. In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise...
2nd Edition. — The MIT Press, 2019. — 504 р. — (Adaptive Computation and Machine Learning series). — ISBN: 978-0262039406. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It...
Amazon Digital Services LLC, 2019. — 46 p. Order Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science, NumPy, Scikit Learn, Pandas and Tensorflow now to learn all the basic concepts you need to know about machine learning and Python. The purpose of...
Amazon Digital Services LLC, 2018. — 50 р. Excited about computer science, machine learning and programming? This book is the ultimate guide to Python programming for beginners and intermediates! Considering that Python is used by big tech companies like Google, Nokia, IBM, Yahoo!, Pinterest, Disney and many others, it makes a lot of sense to prioritize learning Python if you...
Packt Publishing, 2022. - 376 p. - ISBN 180324710X. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level. Key Features Design distributed systems that can be applied to real-world federated learning applications at scale. Discover multiple aggregation schemes applicable to...
Manning Publications Co, 2021. — 493 p. — ISBN 9781617295645. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs....
Manning Publications, 2020. — 360 p. — ISBN: 978-1617295645. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core...
CRC Press, 2024. — 133 p. Machine Learning (ML) and Deep Learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in...
New York: Springer, 2010. — 527 p. This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by...
San Francisco: No Starch Press, 2024. — 263 p. — ISBN 1718503768. Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast...
Packt Publishing, 2017. — 501 p. — ISBN: 978-1787125933. Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is...
Springer Singapore, 2024. — 127 p. — (Studies in Computational Intelligence, volume 1155). — eBook ISBN 978-981-97-2720-9. Covers three primary data types: numerical, textual, and image data Offers GitHub source code encompassing fundamental components and advanced machine learning tools Serves as a reference for researchers, students, practitioners, and policymakers Employing...
Apress, 2018. — 642 p. — ISBN: 978-1-4842-3206-4. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner....
Apress, 2020. — 446 p. — ISBN: 1484258010. Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from...
Packt Publishing, 2020. — 490 p. — ISBN: 9781800208919, 180020891X. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features - Build, train, and deploy machine learning models quickly using...
Packt Publishing, 2021. — 338 p. — ISBN 978-1800204492. Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods...
Springer International Publishing Switzerland. 2014. - 129 p.
Indisputably, Support Vector Machines (SVM) and Evolutionary Algorithms (EA) are both established algorithmic techniques and both have their merits and success stories. It appears natural to combine the two, especially in the context of classification. Indeed, many researchers have attempted to bring them together in...
CreateSpace, 2017. — 266 p. Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Apress, 2017. — 358 p. — ISBN: 978-1484228654. Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six...
CRC Press, 2025. — 171 p. — (Innovations in Multimedia, Virtual Reality and Augmentation). — ISBN 978-1-032-76147-3. Машинное обучение в мультимедиа: раскрытие потенциала визуального и слухового интеллекта This book explores the interdisciplinary nature of Machine Learning in multimedia, highlighting its intersections with fields such as computer vision, natural language...
O’Reilly Media, Inc., 2024. — 300 p. — ISBN: 978-1-098-14463-0. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung,...
Wiley, 2023. — 291 p. — ISBN 9781119898870. Угрозы эксфильтрации данных и методы предотвращения: машинное обучение и безопасность данных на основе памяти Data Exfiltration Threats and Prevention Techniques Comprehensive resource covering threat prevention techniques for data exfiltration and applying machine learning applications to aid in identification and prevention. Data...
Packt Publishing, 2017. — 570 p. — ISBN: 9781785280511. Building Machine Learning applications with R. Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the...
Independently Published, 2023-10-16. — 132 p. — ASIN: B0CL7M4LLZ. "Machine Learning Concepts from A to Z: A Comprehensive Guide with Code" Are you eager to unlock the potential of machine learning, from its fundamental principles to practical implementation? Look no further. "Machine Learning Concepts from A to Z" is your all-encompassing, go-to guide for understanding and...
Palgrave Macmillan, 2023. — 182 p. Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but...
BPB Publications, 2023. — 302 р. — ISBN 978-93-89898-27-9. A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques. Key Features - Understand the core concepts and algorithms of Machine Learning. - Get started with your Machine Learning career with this easy-to-understand guide. - Discover different Machine Learning...
Apress, 2018. - 392p. - ISBN: 1484239504 Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You...
Beijing: Springer-Science Press, 2023. — 213 p. This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsupervised learning, semi-...
Apress, 2020. — 223 p. — ISBN: 978148425982-5. Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel...
Apress, 2020. — 223 p. — ISBN13: (electronic): 978-1-4842-5982-5. Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where...
СПб.: Питер, 2019. — 512 с.: ил. — (IT для бизнеса). — ISBN 978-5-4461-0926-5. Наука о данных становится неотъемлемой частью любой маркетинговой деятельности, и эта книга является живым портретом цифровых преобразований в маркетинге. Анализ данных и интеллектуальные алгоритмы позволяют автоматизировать трудоемкие маркетинговые задачи. Процесс принятия решений становится не...
Springer Cham, 2024. — 522 p. — eBook ISBN 978-3-031-53282-5. Simple and intuitive discussions of probability and statistics Discusses details of applications of mathematical concepts to machine learning Provides mathematical details without losing the reader in complexity This book covers probability and statistics from the machine learning perspective. The chapters of this...
O’Reilly, 2020. — 150 p. — ISBN: 978-1-492-04510-6. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn...
Apress Media LLC., 2020. — 262 p. — ISBN13: (electronic): 978-1-4842-5772-2. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine...
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur. — 2nd Edition. — Packt Publishing, 2020. — 488 p. — ISBN: 978-1-80020-904-6. Code files only! Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms You already know you want to understand supervised learning, and a smarter way to do that is to...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 432 p. — ISBN: 978-1-119-64225-1 (ebk). Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by...
CRC Press, 2023. — 308 p. This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts...
Springer, 2021. - 108p. - ISBN 9783030683788 This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in...
Packt Publishing, 2015. — 190 p. — ISBN: 978-1-78439-908-5. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine...
Mercury Learning & Information, 2020. — 339 p. — ISBN: 978-1-68392-467-8. This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures...
Springer Cham, 2024. — 119 p. — (Intelligent Systems Reference Library, volume 255). — eBook ISBN 978-3-031-59811-1. Presents the novel logo detection methods using machine learning paradigms Demonstrates the merits of the presented approaches over the reported approaches using the real-world applications Includes the state-of-the-art machine learning paradigms This book...
CRC Press, 2025. — 189 p. Bayesian data analysis and modelling linked with Machine Learning (ML) offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization. Two types of correlation play central roles in geotechnical site characterization: cross-correlation among...
Packt Publishing, 2017. — 376 p. — ISBN: 978-1788398435. Extract patterns and knowledge from your data in easy way using MatLAB. Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MatLAB Understand how your data works and identify...
Independently published, 2020. — 201 p. — ISBN: 979-8611356791. Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? Or perhaps you want to know how to analyze data to solve simple domestic issues but you don’t know how to do it? Here’s the deal… As a beginner you will be probably afraid that...
Packt Publishing, 2019. — 360 p. — ISBN: 978-1-83864-535-9. Code files only! This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. AI is changing the world – and with this book, anyone can start building intelligent software! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of...
Springer, 2012. — 81 p. — ISBN 978-1-4471-2977-6, e-ISBN 978-1-4471-2978-3. This chapter presents a brief introduction to entropy guided transformation learning (ETL), a machine learning algorithm for classification tasks. ETL generalizes transformation based learning (TBL) by automatically solving the TBL bottleneck: the construction of good template sets. The main advantage...
Pringer, 2023. — 145 p. — (The Information Retrieval Series 47). — ISBN 978-3031204661. Обучение количественной оценке This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify...
Independently published, 2024. — 181 p. — ISBN-13: 979-8332452369. This book delves into the intricate relationship between mathematics and machine learning, providing readers with a comprehensive understanding of the mathematical concepts that underpin modern AI. From linear algebra and calculus to probability theory and statistics, each chapter explores a different...
O’Reilly, 2023. — 360 p. — Second Edition. This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural...
BPB Publications, 2021. — 218 p. — ISBN: 978-93-90684-700. Utilize Python and IBM Watson to put real-life use cases into production. Key Features Use of popular Python packages for building Machine Learning solutions from scratch. Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. Expert-led best practices to put...
Springer, 2022. — 161 p. — ISBN 978-3-031-14634-3. Анализ данных с помощью машинного обучения для психологов: ускоренный курс по изучению Python 3 и машинного обучения за 10 часов The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of...
The MIT Press, 2020. — 304 p. — ISBN 9780262358019. An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key...
Springer, 2020. — 693 p. — ISBN: 9811549354. This book features selected papers presented at the International Conference on Information Management and Machine Intelligence (ICIMMI 2019), held at the Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India, on December 14–15, 2019. It covers a range of topics, including data analytics; AI; machine and deep...
Morgan Kaufmann/Elsevier, 2024. — 408 p. Synthetic Data and Generative AI covers the foundations of Machine Learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including...
Springer, 2022. — 78 p. — (SpringerBriefs in Computer Science). — ISBN 978-981-19-4873-2. Использование машинного обучения в программно-определяемых сетях Emerging Machine Learning (ML) techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of...
IGI Global, 2024. — 530 p. The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by Artificial Intelligence (AI) and Machine Learning (ML) technologies in the development of intelligent machines. As the demand for intelligent machines continues to rise across various sectors, understanding the...
O’Reilly Media, 2023. — 600 p. The past decade has witnessed the broad adoption of Artificial Intelligence and Machine Learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit,...
The MIT Press, 2007. — 119 p. I had the great priviledge of taking the class upon which this book was based last semester at Princeton University under professors Harman and Kulkarni. It is a fascinating little book, which manages to distill decades of debate and research into concise, readable chapters that carry the presentation forward. The authors' approach is original but...
Apress, 2017. — 204 p. - ISBN: 978-1-4842-2823-4 Code files only! Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business...
New York: Springer, 2021. — 640 p. This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with...
Boston: MIT Press, 2017. — 412 p. A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a...
Springer, 2008. — 173 p. The book presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more...
Frank Hutter, Lars Kotthof, Joaquin Vanschoren. — Springer, 2019. — 219 p. — ISBN: 978-3-030-05318-5. В этой книге представлен первый всеобъемлющий обзор общих методов автоматического машинного обучения (AutoML), собраны описания существующих систем на основе этих методов и обсуждена первая серия международных проблем систем AutoML. Недавний успех коммерческих приложений...
O’Reilly Media, 2022. — 386 p. — ISBN-13: 978-1-098-10796-3. Machine learning systems are both complex and unique. They are complex because they consist of many different components and involve many different stakeholders. They are unique because they are data-dependent, and data varies wildly from one use case to the next. This book takes a holistic approach to designing...
The MIT Press, 2017. — 624 p. — ISBN: 026203641X. The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage,...
Packt Publishing, 2019. — 448 p. — ISBN: 978-1-78934-634-3. Code files only! Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the...
Boca Raton: CRC Press, 2022. — 397 p. — ISBN 9781032036724. Rapid population growth coupled with the evolution of numerous diseases is a matter of concern worldwide. Due to this, the healthcare industry has emerged as an essential service sector. The generation of a large amount of healthcare data and the lack of insight from that data are significant problems in the healthcare...
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu. — Packt Publishing, 2019. — 416 p. —ISBN: 978-1-83855-013-4. Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems With huge amounts of data being generated every moment, businesses need applications that apply complex...
Packt Publishing, 2017. — 420 p. — ISBN: 978-1-78728-074-8. Key Features Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Book Description Join...
O’Reilly, 2023. — 266 p. — (Sixth Early Release). Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. These systems treat...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN 978-1-492-08525-6. • 2021-12-07: First Release Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning...
2nd Edition. — Packt Publishing, 2019. — 240 p. — ISBN: 978-1-78899-459-0. Bring magic to your mobile apps using TensorFlow Lite and Core ML Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with...
New York: Apress, 2018. — 355 p. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine...
Packt Publishing, 2022. — 395 p. — ISBN 1800566018. Build predictive models using large data volumes and deploy them to production using cutting-edge techniques. Key Features Build highly accurate state-of-the-art machine learning models against large-scale data. Deploy models for batch, real-time, and streaming data in a wide variety of target production systems. Explore all...
Springer Vieweg, 2023. — 132 p. — ISBN 3658404418. Machine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities , in which they could be deceived to return preferred predictions....
O'Relly, 2021. — 156 р. — ISBN: 978-1098115715. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the...
3rd Edition. — Packt Publishing Ltd., 2019. — 458 p. — ISBN: 978-1-78829-586-4. Code files only! Solve real-world data problems with R and machine learning Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R,...
Packt Publishing, 2017. — 254 p. — ISBN13: 978-1783553112. Take tiny steps to enter the big world of data science through this interesting guide. Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever....
Orange Education Pvt. Ltd , 2024. — 247 р. — ISBN 8197256373, 978-8197256370, ASIN B0D8L3Q283. Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it...
IGI Global, 2020. — 344 p. — (Advances in Computer and Electrical Engineering). — ISBN 978-1799830955. Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock...
Springer, 2021. — 349 p. — (Algorithms for Intelligent Systems). — ISBN 978-981-33-4045-9. This book comprises the best deliberations with the theme “Machine Learning Technologies and Applications” in the “International Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2020),” organized by the Department of Computer Science and Engineering, VNR...
Boca Raton: CRC Press, 2025. — 361 p. Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of...
Springer, 2022. — 254 p. This book introduces machine learning and its applications in smart environments/cities. At this stage, a comprehensive understanding of smart environment/city applications is critical for supporting future research. This book includes chapters written by researchers from different countries across the globe and identifies critical threads in research...
Packt Publishing, 2021. — 737 p. — ASIN B08PDFXXRL. Code Files Only! Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become...
Abiprod Pty, 2018. — 125 p. — ISBN: 1718095031. Are you excited about Artificial Intelligence and want to get started?Are you excited about Machine Learning and want to learn how to implement in Python? The book below is the answer. Given the large amounts of data we use everyday; whether it is in the web, supermarkets, social media etc. analysis of data has become integral to...
Cambridge: Cambridge University Press, 2022. — 337 p. Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin...
Packt, 2019. — 403 p. Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features Implement data science and machine learning techniques to draw insights from real-world data Understand what IBM Cloud platform can help you to implement cognitive insights within applications Understand the role of data representation and...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-52-7. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence...
Manning Publications, 2021. — 426 p. — ISBN 978-1617296741. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction...
Manning Publications, 2021. — 426 p. — ISBN 9781617296741. Code Files Only! Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the...
2nd Edition. — John Wiley & Sons, 2021. — 464 p. — (For Dummies). Your comprehensive entry-level guide to machine learning. While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android?as in the movie Ex Machina?it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and...
IGI Global Scientific Publishing, 2025. — 504 p. Description: In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed...
AI Sciences, 2018 — 190p. — ISBN13: 978-1724417503. This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests...
Packt Publishing, 2018. — 378 p. — ISBN: 978-1-78883082900. Leverage the power of Apple's Core ML to create smart iOS apps Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple,...
Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. — 124 p. — ISBN: 978-3-662-48838-6. The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical...
Independently published, 2021. — 201 p. Introduction. What Is Machine Learning? Machine Learning - Concepts & Terms. Linear Regression with Python. Lists In Python. Modules In Python. Machine Learning Training Model. Conditional or Decision Statements. Essential Libraries for Machine Learning in Python. What is the TensorFlow Library. Artificial Neural Networks. Conclusion.
O’Reilly, 2019. — 337 p. — ISBN: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where...
Packt Publishing, 2019. — 342 p. — ISBN: 978-1-78899-692-1. This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning...
Basel: Birkhäuser, 2022. — 160 p. This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively...
Cham: Springer International Publishing, 2019. — 107 p. — ISBN: 978-3-319-98675-3. This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations,...
O’Reilly Media, 2023. — 380 p. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run...
BPB Publications, 2023. — 302 р. — ISBN 978-93-89898-27-9. A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques. Key Features - Understand the core concepts and algorithms of Machine Learning. - Get started with your Machine Learning career with this easy-to-understand guide. - Discover different Machine Learning...
Abdul Rahman, Christopher Redino, Sachin Shetty, Dhruv Nandakumar, Tyler Cody, Dan Radke. — Wiley-IEEE Press, 2025. — 288 p. — ISBN-13: 978-1394206452. A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration...
CreateSpace Independent Publishing Platform, 2017. — 56 p. The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple.From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept...
Lioncrest Publishing, 2021. — 222 p. — ISBN 978-1-5445-1882-4. How can you successfully deploy AI? When AI works, it’s nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren’t...
Wiley, 2025. — 409 p. — ISBN 9781394294374. PDF (true) An essential guide for tackling outliers and anomalies in Machine Learning and Data Science. In recent years, Machine Learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust Machine Learning is a new approach to handling outliers in datasets,...
Packt Publishing, 2019. — 327 р. — ISBN 978-1-78913-660-9. Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble...
Springer, 2021. — 164 p. This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts...
SAS Institute, 2022. — 364 p. Discover how to build decision trees using SAS Viya! Tree-Based Machine Learning Methods in SAS Viya covers everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests,...
Springer, 2025. — 344 p. — (Cognitive Technologies). — ISBN 978-3-031-83096-9. This book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with...
Packt, 2020. — 246 p. — ISBN: 9781838646486. Code files only! Build secure private blockchain networks to handle mission-critical security challenges such as denial-of-service attacks, user wallets, and pool mining attacks Key Features Explore blockchain concepts such as cryptography, consensus algorithms, and security assumptions Architect network security for mission-critical...
O’Reilly Media, 2023. — 450 р. — ISBN 978-1-098-12020-7. Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can...
Independently published, 2019. — 128 р. — ISBN: 1092554556. A baby learns to crawl, walk and then run. we are in the crawling stage when it comes to appllying Machine Learning. Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that makes...
IGI Global, 2024. — 327 p. — (Advances in Computational Intelligence and Robotics (ACIR) Book Series). — ISBN 979-8-3693-5271-7. In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative...
Packt Publishing, 2021. — 338 p. — ISBN 978-1800204492. Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods...
Apress, 2017. — 358 p. — ISBN: 978-1484228654. Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six...
O’Reilly Media, 2020. — 432 p. — ISBN: 978-1-492-07305-5. Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised,...
Oliver Theobald, 2017. — 52 p. The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the...
Springer, 2021. — 504 p. — ISBN 9811595151. This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2020), held jointly by Vignan’s Institute of Information Technology, Visakhapatnam, India and VFSTR Deemed to be University, Guntur, AP, India during 03-04 September 2020. Topics covered in the book include...
O’Reilly Media, Inc., 2024. — 345 p. — ISBN 978-1-098-15161-4. What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that...
Packt Publishing, 2019. — 484 p. — ISBN: 978-1-78953-414-3. Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book,...
Leanpub, 2019. — 133 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
Morgan & Claypool, 2018. — 169 p. — ISBN: 9781681733968 (ebook). Synthesis Lectures on Artificial Intelligence and Machine Learning The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language,...
Packt, 2022. — 542 p. — ISBN 1803241675, 9781803241678. Explore supercharged machine learning techniques to take care of your data laundry loads Key Features Learn how to prepare data for machine learning processes Understand which algorithms are based on prediction objectives and the properties of the data Explore how to interpret and evaluate the results from machine learning...
Berlin: Springer, 2005. - 434 p.
This volume is composed of 20 chapters selected from the recent myriad of novel SVM applications, powerful SVM algorithms, as well as enlightening theoretical analysis. Written by experts in their respective fields, the first 12 chapters concentrate on SVM theory, whereas the subsequent 8 chapters emphasize practical applications, although the...
CRC Press, 2024. — 287 p. Editors: Dr Pawan Whig, Dean Research, Vivekananda Institute of Professional Studies-TC, India Dr Pavika Sharma, Assistant Professor, Bhagwan Parshuram Institute of Technology, India Dr Nagender Aneja, Program Leader (Computer Science), Universiti Brunei Darussalam, Brunei Dr Ahmad A. Elngar, Head of Computers and AI, Beni-Suef University, Egypt Dr...
Boca RAton: CRC Press, 2020. — 329 p. The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR...
Springer, 2015. — 284 p.
The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has...
ITexLi, 2023. — 177 p. — ISBN 1837687161 9781837687169 1837687153 9781837687152 183768717X 9781837687176. Rapid advancements in technology have had a significant impact on teaching and learning. Globally, we have experienced an increase in innovative pedagogies and disruptive learning spaces, which have transformed teaching and learning. Tremendous changes in educational...
М.: ДМК Пресс, 2018. — 252 с. Н2О - простая в использовании и открытая библиотека, которая поддерживает большое количество операционных систем и языков программирования, а также масштабируется для обработки больших данных. Эта книга научит вас использовать алгоритмы машинного обучения, реализованные в Н2О, с упором на наиболее важные для продуктивной работы аспекты. Рассмотрены...
Springer, 2023. — 235 р. — ISBN 978-3-031-18552-6. The Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modelling, and Applications presents the state of the art of the application of Machine Learning (ML) and Deep Learning (DL) in the domain of finance. We will present a combination of empirical evidence to diverse fields of finance so...
ITexLi, 2023. — 132 p. — ISBN 0850145147 9780850145144 0850145139 9780850145137 0850145155 9780850145151. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these...
2nd Edition. — Springer, 2022. — 582 p. — ISBN 978-3-030-96622-5. This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book...
AI Publishing LLC, 2020. — 301 p. — ISBN B08LSLHBR8. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce,...
O’Reilly Media, Inc., 2018. — 366 p. — ISBN: 978-1-491-98938-8. Целевая аудитория: опытные разработчики. Пожалуй, ни для кого не новость, что Python в последнее время популярен именно как инструмент для разработки в области машинного обучения и Data Science. Это руководство рассматривает современные подходы языка к решению актуальных проблем в данной области, принятые стандарты...
Independently published, 2021. — 171 p. — ISBN 979-8724724272. Machine Learning is rapidly changing the world and it is the way of the Future of Human Technology. Art, information, processes, calculations, emotions will be rapidly learned and discover from machines. Do you want to learn more about the world of Machine Learning and its Applications? Would you like to improve and...
Independently published, 2024. — 92 p. This comprehensive guide is tailored to address precisely those concerns and assist you in navigating through the intricacies of Python Machine Learning. In "Python Machine Learning: A Comprehensive Beginner's Guide with Scikit-Learn and Tensorflow," you will embark on a journey to unravel the mysteries of: Understanding the essence of...
Apress, 2018. — 362 p. — ISBN13: (electronic): 978-1-4842-3564-5. Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm...
Springer, 2023. — 768 p. — (Genetic and Evolutionary Computation) — eBook ISBN: 978-981-99-3814-8. Explores various ways evolution can help improve current methods of machine learning Presents real-world applications in medicine, robotics, science, finance, and other domains Serves as an essential reference for those interested in evolutionary approaches to machine learning...
Tarkeshwar Barua, Kamal Kant Hiran, Ritesh Kumar Jain, Ruchi Doshi. — Walter de Gruyter, 2024. — 487 p. — (De Gruyter STEM)/ — ISBN 13: 9783110697162. This book explains how to use the programming language Python to develop machine learning and deep learning tasks. It provides readers with a solid foundation in the fundamentals of machine learning algorithms and techniques. The...
Springer, 2024. — 173 p. — (Springer INdAM Series 61). — ISBN 978-981-97-6768-7. Передовые методы оптимизации машинного обучения и обработки изображений In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of Machine Learning and imaging. The present book is a collection of recent contributions in the field of...
Springer, 2018 - 257 p. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based...
2nd Edition. — BPB Publications, 2024. — 472 р. — ASIN: B0CLL37MFL. The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in Machine Learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection,...
Groningen: University of Groningen Press, 2023. — 292 p. — ISBN 9789403430270. The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general . However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus...
Albert Bifet, Ricard Gavalda, Geoff Holmes, Bernhard Pfahringer. — MIT Press, 2018. — 288 p. — ISBN 978-0-262-03779-2. A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources―including sensor networks, financial markets, social...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Packt Publishing, 2017. — 449 p. — ISBN: 978-1785889622. Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering...
2nd Edition. — Packt Publishing, 2018. — 514 p. — ISBN: 978-1-78934-799-9. Code files only! Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Machine learning has gained tremendous popularity for its powerful...
2nd Edition. — Packt Publishing, 2020. — 848 p. — ISBN: 978-1-83882-029-9. Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 418 p. — ISBN: 978‐1‐119‐56195‐8 (ebk). This book, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and...
Packt Publishing, 2015. — 190 p. — ISBN: 978-1-78439-908-5. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine...
Independently published, 2018. — 79 р. Do you want to impress the processes that you are working on? Do you want to make your machines more intelligent? If your answer to any of those questions is yes, then you have come to the right place. This book is a sequel to the book titled 'Machine Learning: A Step-by-Step guide.' In the first book, you gathered information on what...
2nd edition. — Morgan & Claypool Publishers, 2018. — 284 p. — ISBN: 9781681733999. Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation:...
2nd ed. — Packt Publishing, 2015. — 305 p. — ISBN: 9781784392772. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of...
Amazon.com Services LLC., 2020. — 151 p. Become the master of machine learning with this powerful guide. Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin? Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple,...
2nd Edition. — Packt Publishing, 2019. — 378 р. — ISBN: 978-1788994170. Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the...
O’Reilly Media, 2011. — 148 p. — ISBN: 9781449314309. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation. This book also includes a short tutorial on...
O’Reilly Media, 2016. — 290 р. — ISBN: 978-1491964606. In Practical Machine Learning with H 2 O, author Darren Cook introduces readers to H 2 O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H 2 O, import and export data, and distinguish H 2 O algorithms. Readers will...
O’Reilly Media, 2016. — 290 р. — ISBN: 978-1491964606. In Practical Machine Learning with H 2 O, author Darren Cook introduces readers to H 2 O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H 2 O, import and export data, and distinguish H 2 O algorithms. Readers will...
Independently Published, 2020. — 107 p. Are you looking for an easy to follow guide for learning Python programming for Machine Learning? In this book Marvin Dale is going to teach you the basics and some advanced tricks for learning Python and Machine Learning. "Machine Learning is the future". You have surely heard that sentence, from the news or from a friend. You also have...
Packt Publishing, 2019. — 360 p. — ISBN: 978-1-83864-535-9. Code files only! This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. AI is changing the world – and with this book, anyone can start building intelligent software! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of...
Manning Publications, 2020. — 220 p. — ISBN: 978-1617294884. How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore...
Microsoft Press, 2020. — 392 p. — ISBN 9780135565667. Sample files only! Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and...
Microsoft Press, 2020. — 450 p. — ISBN: 978-0-13-556566-7. Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic...
Microsoft Press, 2020. — 450 p. — ISBN: 978-0-13-556566-7. Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic...
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to...
Amazon.com Services LLC, 2020. — 120 p. — ISBN: 979-8615125096. You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you’ve always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and...
Springer, 2021. — 293 p. — ISBN 978-3030712693. This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select...
Routledge, 2023. — 191 p. — ISBN: 978-1-032-07277-7. Машинное обучение для бизнес-аналитики: анализ данных в реальном времени для принятия решений Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data...
Springer, 2023. — 102 p. This book is a state of the art resource that robotics researchers and engineers can use to make their robots and autonomous vehicles smarter. Readers will be able to describe metareasoning, select an appropriate metareasoning approach, and synthesize metareasoning policies. Metareasoning for Robots adopts a systems engineering perspective in which...
Frank Hutter, Lars Kotthof, Joaquin Vanschoren. — Springer, 2019. — 219 p. — ISBN: 978-3-030-05318-5. В этой книге представлен первый всеобъемлющий обзор общих методов автоматического машинного обучения (AutoML), собраны описания существующих систем на основе этих методов и обсуждена первая серия международных проблем систем AutoML. Недавний успех коммерческих приложений...
O’Reilly Media, 2022. — 386 p. — ISBN-13: 978-1-098-10796-3. Machine learning systems are both complex and unique. They are complex because they consist of many different components and involve many different stakeholders. They are unique because they are data-dependent, and data varies wildly from one use case to the next. This book takes a holistic approach to designing...
Packt Publishing, 2019. — 456 p. — ISBN: 978-1-78913-636-4. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms...
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight from volumes of...
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. Code files only! With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight...
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. Code files only! With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight...
Springer, 2023. — 137 p. — (Synthesis Lectures on Learning, Networks, and Algorithms). — ISBN 978-3-031-19066-7. Алгоритмы оптимизации для распределенного машинного обучения Stochastic gradient descent (SGD) is the backbone of supervised Machine Learning training today. Classical SGD was designed to be run on a single computing node, and its error convergence with respect to...
Packt Publishing, 2016. — 405 p. — ASIN B01BSTEDKY. Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range...
Packt Publishing, 2016. — 258 p. — ISBN: 978-1-78439-658-9. Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning...
Packt, 2018. - 356p. - ISBN: 9781788998246 Code files only! A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in...
Packt Publishing, 2018. — 367 p. — ISBN: 978-1-78899-824-6. A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN 978-1-492-08525-6. • 2021-12-07: First Release Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning...
CRC Press, 2025. – 300 p. – ISBN 978-1-032-55232-3. This book presents the research into and application of Machine Learning in quantum computation, known as Quantum Machine Learning (QML). It presents a comparison of Quantum Machine Learning, classical Machine Learning, and traditional programming, along with the usage of quantum computing, toward improving traditional Machine...
Apress, 2020. — 146 p. — ISBN13: (electronic): 978-1-4842-5940-5. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain...
New York: Morgan Kaufmann, 1996. — Recent years have seen an explosion of work on machine learning, the computational study of algorithms that improve performance based on experience. Research on rule induction, neural networks, genetic algorithms, case-based reasoning, and probabilistic inference has produced a variety of robust methods for inducing knowledge from training...
Second Edition. — Independently published, 2021. — 281 p. — ISBN 978-1838132217. This Second Edition improves clarity of code, general formatting, text explanations as well as updating the AI for 2021 stock selection. Build Your Own AI Investor Breaks down Value Investing for the AI revolution, whilst being accessible to anyone, even if you've never invested in stocks or coded...
O’Reilly Media, Inc., 2023. — 290 p. — ISBN-13: 978-1-098-10665-2. With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph,...
Springer, 2023. — 189 p. This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including: blind source separation; social network mining; image and video processing; array...
Springer, 2024. — 162 p. This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention...
Springer, 2022. — 288 p. — ISBN 978-981-16-9839-2, 978-981-16-9840-8. Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained...
Packt Publishing, 2017. — 292 p. — ISBN10: 1783553111, ISBN13: 978-1783553112. Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book...
Wiley-IEEE, 2020. — 496 p. — ISBN 978-1119562313(EPUB). A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the...
Packt Publishing, 2021. — 344 p. — ISBN 978-1-80107-812-2. Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key Features Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML Leverage SQL syntax to train, evaluate,...
Independently published, 2023. — 245 p. In the ever-changing world of finance and trading, the search for a competitive edge has been a constant driver of innovation. Over the last few decades, the field of quantitative trading has emerged as a powerful force, pushing the boundaries of what is possible and reshaping the way we approach the market. At the heart of this...
Academic Press/Elsevier, 2023. — 222 р. — ISBN: 978-0-443-19035-3. Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous...
Birmingham: Packt Publishing, 2021. — 312 p. — ISBN 9781800567689. Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning Key Features Get up to speed with AutoML using the platform of your choice, such as OSS, Azure, AWS, or GCP. Eliminate mundane tasks in data engineering and reduce human errors in ML...
Apress, 2021. — 284 p. — ISBN 978-1484270226. Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights)....
Packt Publishing, 2019. — 293 p. — ISBN: 978-1-78934-979-5. Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing...
2nd Edition. — Leanpub, 2022-03-04. — 329 р. This book teaches you how to make Machine Learning models more interpretable. Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of Machine Learning. This book is about making Machine Learning models and their...
Manning Publications, 2021. — 426 p. — ISBN 9781617296741. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction...
O’Reilly Media, Inc., 2023. — 279 p. — ISBN 978-1-098-11913-3. Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for...
Packt Publishing, 2018. — 378 p. — ISBN: 978-1-78883082900. Leverage the power of Apple's Core ML to create smart iOS apps Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple,...
Учебно-методическое пособие. — СПб: Университет ИТМО, 2024. – 41 с. This manual aims to provide some basic knowledge on Machine Learning algorithms for the Infochemistry Scientific Center students. It is a practice-oriented tutorial which serves as guideline for the beginners in their desire to build their own Machine Learning models. The Database (DB) used in this work...
Springer, 2021. — 769 p. — ISBN 978-3-030-70541-1. This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and...
3rd Edition. — Apress Media LLC., 2024. — 463 р. — ISBN 978-1-4842-9846-6. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem...
BPB Publictions, 2019 — 280 p. — ISBN: 978-93-88511-13-1. Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite...
Apress, 2019. — 368 р. — ISBN: 978-1-4842-3799-1. Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data...
Genoa: University of Genoa, 2007. — 101 p. Many approaches for obtaining systems with intelligent behavior are based on components that learn automatically from previous experience. The development of these learning techniques is the objective of the area of research known as machine learning. During the last decade, researchers have produced numerous and outstanding advances in...
O’Reilly Media, 2021-12-10. — 362 p. — ISBN-13: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world’s data is unlabeled, conventional supervised learning cannot be applied;...
Springer, 2008. — 375 p. 5th InternationalWorkshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008. Proceedings. The 32 papers gathered in this volume cover a wide range of topics related to human-human communication modeling and processing, as well as to human-computer interaction, using several communication modalities. A significant number of papers focus on the...
Springer Cham, 2023. — 451 p. — (Computational Methods in Engineering & the Sciences) — eBook ISBN: 978-3-031-36644-4. Comprehensive state-of-the-art book on scientific machine learning approaches in modelling & simulation Covers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modelling & material design Focuses on engineering applications in...
Business Expert Press, LLC, 2025. — 264 p. — ISBN-13 978-1-63742-749-1. EPUB (true) Machine Learning Fundamentals provides a comprehensive overview of Data Science, emphasizing Machine Learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed. Topics...
Packt, 2020. — 351 p. — ISBN: 9781839219061. Take a comprehensive and step-by-step approach to understanding machine learning Key Features Discover how to apply the scikit-learn uniform API in all types of machine learning models Understand the difference between supervised and unsupervised learning models Reinforce your understanding of machine learning concepts by working on...
Apress Media, LLC, 2025. — 346 p. — (Maker Innovations Series). — ISBN-13: 979-8-8688-1293-4. Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform. You’ll start by exploring Machine learning in the context of...
Manning Publications Co., 2021. — 513 p. — ISBN 978-1617295911. Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble...
Boca Raton: CRC Press, 2023. — 357 p. This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques...
Wiley-Scrivener, 2024. — 489 p. The book provides a comprehensive overview of current research and developments in the field of Deep Learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of Deep Learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep Learning helps...
2nd Edition. — Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce. — Wiley, 2023. — 611 p. — ISBN 978-1119903833. MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,...
Oxford University Press, 2022. — 401 p. — ISBN 978–0–19–284529–0. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders...
2nd Edition. — Balige Publishing, 2023. — 525 p. In this project, we will perform an analysis and prediction task on stroke data using Machine Learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to...
BALIGE Publishing, 2022. — 533 p. Welcome to “Machine Learning for Concrete Compressive Strength Analysis and Prediction with Python.” In this book, we will explore the fascinating field of applying machine learning techniques to analyze and predict the compressive strength of concrete. First, we will dive into the dataset, which includes various features related to concrete...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
Apress Media LLC, 2020. — 300 p. — ISBN13: (electronic): 978-1-4842-5967-2. Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE. You’ll start...
Morgan Kaufmann, 2019. — 327 p. — ISBN: 978-0-12-814623-1. Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional...
Apress, 2020. - 150p. - ISBN 1484265459 This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models....
Amazon Digital Services, 2017. — 61 p. This book is an introduction to basic machine learning and artificial intelligence. It gives you a list of applications, and also a few examples of the different types of machine learning. Here's What You'll Learn in this Book: – Introduction to Machine Learning – Different Applications of Machine Learning – Introduction to Statistics for...
IGI Global, 2020. — 674 p. — ISBN 1522596437. As todays world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to...
Manning Publications Co., 2022. — 339 p. — ISBN: 978-1617298059. Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input. Optimize every stage of your machine learning pipelines with powerful automation...
Boca Raton: CRC Press, 2023. — 261 p. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and...
2nd edition. — CRC Press, 2023. — 498 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — eBook ISBN 978-1-003-26487-3. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic...
CRC Press, 2024. — 99 p. Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where...
Springer, 2020. — 292 p. — ISBN: 978-3-030-37830-1. This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power...
The MIT Press, 2022. — 325 p. — ISBN 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard Machine Learning (ML) techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely...
CreateSpace, 2017. — 266 p. Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Springer, 2019. — 155 p. — ISBN: 981133028X This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis....
New York: Springer, 2022. — 203 p. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.
Oliver Theobald, 2017. — 52 p. The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the...
Packt Publishing, 2019. — 484 p. — ISBN: 978-1-78953-414-3. Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book,...
Reactive Publishing, 2024. — 497 p. — (Python Libraries for Finance Book 9). — ASIN: B0D66BR1B1. "Machine Learning: Scikit-Learn for Finance" bridges the gap between the complex world of machine learning and practical financial applications. With a focus on hands-on examples and real-world scenarios, this book is designed to equip readers with the skills to implement...
John Wiley & Sons Inc., 2024. — 512 p. — ISBN 978-1394220625. Теория и приложения машинного обучения: практические примеры использования Python на классических и квантовых машинах Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications...
Independently published, 2019. — 129 p. — ISBN: 978-1075281518. Do you want to learn about machine learning? Are you feeling left out in the rat race where everyone is brushing up on their knowledge in the fields of AI and machine learning? Are you on a time crunch and don’t have enough time or resources to take a formal course on machine learning? If the answer to any of the...
Springer, 2023. — 319 p. Machine Learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as Machine Learning has many...
World Scientific Publishing, 2023. — 418 p. — ISBN 9781800613690. The juxtaposition of "Machine Learning" and "pure mathematics and theoretical physics" may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated...
Packt Publishing, 2015. — 405 p. — ISBN13: 978-1783982042. About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset from the churn dataset,applying different classification methods. Who This Book Is For If you want to learn how to use R for...
Wiley-IEEE Press, 2024. — 275 p. — ISBN 978-1-394-18852-9. Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively;...
Apress, 2020. — 223 p. — ISBN13: (electronic): 978-1-4842-5982-5. Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where...
Автор, 2023. — 110 с. Практическое руководство, предназначенное для всех, кто хочет войти в мир машинного обучения и освоить его основы. Авторы книги предлагают читателям увлекательное путешествие в эту захватывающую область, начиная с основных концепций и принципов машинного обучения и заканчивая практическими навыками построения и обучения моделей. Внутри книги читатели...
Автор, 2023. — 110 с. Практическое руководство, предназначенное для всех, кто хочет войти в мир машинного обучения и освоить его основы. Авторы книги предлагают читателям увлекательное путешествие в эту захватывающую область, начиная с основных концепций и принципов машинного обучения и заканчивая практическими навыками построения и обучения моделей. Внутри книги читатели...
Ташкент, 2015. — 27 с. Теория обобщающей способности - ТОСАМО является альтернативной для наиболее распространённой на сегодняшний день Статистической теории машинного обучения Владимира Вапника. ТОСАМО с помощью математической теории игр и линейной алгебры отвечает на вопросы, не имевшие ответов в предшествующих теориях: 1. Почему максимальная обучающая способность не не всегда...
Springer, 2023. — 235 р. — ISBN 978-3-031-18552-6. The Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modelling, and Applications presents the state of the art of the application of Machine Learning (ML) and Deep Learning (DL) in the domain of finance. We will present a combination of empirical evidence to diverse fields of finance so...
AI Sciences LLC, 2018. — 132 р. — ASIN B07GMVP3WP. Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the...
Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, Nisheeth Joshi. — John Wiley & Sons, Inc., 2022. — 271 p. — ISBN 978-1119775614. Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML)...
CRC Press, 2021. — 339 p. — ISBN: 978-0367336745. The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book...
CRC Press, 2021. — 339 p. — ISBN: 978-0429322990 (ebk). The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools....
CRC Press, 2021. — 339 p. — ISBN: 978-0429322990 (ebk). The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools....
CRC Press, 2021. — 339 p. — ISBN: 978-0429322990 (ebk). The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools....
IGI Global, 2023. — 385 p. — ISBN-13: 978-1668456446. In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address...
CRC Press, 2023. — 310 p. This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on...
Apress Media LLC., 2020. — 262 p. — ISBN13: (electronic): 978-1-4842-5772-2. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine...
Apress, 2018. — 362 p. — ISBN13: (electronic): 978-1-4842-3564-5. Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm...
Springer, 2023. — 327 p. — ISBN: 978-981-19-5169-5. Настройка гиперпараметров для машинного и глубокого обучения с помощью R: практическое руководство Hyperparameter tuning? Is this relevant in practice? Is it not rather an academic gimmick? This book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 432 p. — ISBN: 978-1-119-64225-1 (ebk). Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by...
O’Reilly Media, 2016. — 82 p. — ISBN: 978-1-491-96297-8. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding...
BPB Publications, 2020 — 262 p. — ISBN 978-93-89845-42-6. Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key Features ● Understand the types of Machine learning. ● Get familiar with different Feature extraction methods. ● Get an overview of how Neural Network Algorithms work. ● Learn how to implement Decision Trees and Random Forests. ●...
2nd Edition. — Packt Publishing, 2018. — 300 p. — ISBN: 978-1788474399. Key Features Solve predictive modeling problems using the most popular machine learning Java libraries Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries Practical examples, tips, and tricks to help you understand applied machine learning in Java Book...
CRC Press, 2020. — 488 p. — ISBN-13: 978-1-138-49568-5. Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R,...
Packt Publishing, 2020. — 740 p. — ISBN: 978-1838820299 2nd.ed. Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time...
Independently published, 2022. — 98 p. — ASIN: B09QYQC31S. Have you thought about a career in data science? It’s where the money is right now, and it’s only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it’s the next logical step. Machine learning is a subsection...
Mercury Learning & Information, 2020. — 363 p. — ISBN: 978-1-68392-495-1. This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is...
Apress, 2019. — 248 p. — ISBN13: (electronic): 978-1-4842-5107-2. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in...
Wiley-IEEE Press, 2024. — 240 p. Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations. Machine Learning Applications discusses methodological advancements of Machine Learning and Deep Learning, presents applications in image processing, including face and vehicle detection, image...
Academic Press/Elsevier, 2023. — 300 p. — ISBN 978-0-12-824020-5. Состязательная устойчивость для машинного обучения Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image...
Packt Publishing, 2019. — 325 p. — ISBN 978-1-78980-794-3. R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within...
Packt Publishing, 2017. — 376 p. — ISBN: 978-1788398435. Extract patterns and knowledge from your data in easy way using MatLAB. Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MatLAB Understand how your data works and identify...
Amazon.com Services LLC., 2020. — 151 p. Become the master of machine learning with this powerful guide. Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin? Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple,...
Packt Publishing, 2016. — 332 p. — ISBN: 978-1-78439-475-2. Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the...
Wiley-IEEE Press, 2025. — 445 р. — ISBN: 978-1394272945. Enables readers to develop foundational and advanced vectorization skills for scalable Data Science and Machine Learning and address real-world problems. Offering insights across various domains such as Computer Vision and natural language processing (NLP), Vectorization covers the fundamental topics of vectorization...
Independently Published, 2020. — 107 p. Are you looking for an easy to follow guide for learning Python programming for Machine Learning? In this book Marvin Dale is going to teach you the basics and some advanced tricks for learning Python and Machine Learning. "Machine Learning is the future". You have surely heard that sentence, from the news or from a friend. You also have...
Packt, 2019. — 360 p. — ISBN: 9781838645359. This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. Key Features Roll up your sleeves and start programming AI models No math, data science, or machine learning background required Packed with hands-on examples, illustrations, and clear step-by-step instructions 5...
Packt Publishing, 2017. — 438 p. — ISBN: 978-1788294041. An effective guide to using ensemble techniques to enhance machine learning models. Key Features Learn how to maximize popular machine learning algorithms such as random forests, decision trees, AdaBoost, K-nearest neighbor, and more Get a practical approach to building efficient machine learning models using ensemble...
Packt Publishing, 2017. — 438p. — ISBN: 978-1788294041. !Code files only An effective guide to using ensemble techniques to enhance machine learning models Key Features Learn how to maximize popular machine learning algorithms such as random forests, decision trees, AdaBoost, K-nearest neighbor, and more Get a practical approach to building efficient machine learning models...
Apress, 2019. - 365p. - ISBN: 9781484236574 Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior...
Springer-Verlag Berlin Heidelberg, 2012. — 345 p. — ISBN 978-3-540-75196-0, ISBN 978-3-540-75197-7 (eBook). It was on a balmy spring day in Nanjing, while we were working on a paper on contrast discovery for the Journal of Machine Learning Research, that Nada Lavraˇc let slip that she was writing a book on rule learning with Johannes F¨urnkranz and Dragan Gamberger. I must...
Leanpub, 2022-08-22. — 155 р. Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable Machine Learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods. Feature...
2nd edition. — O’Reilly Media, Inc., 2019. — 519 p. — ISBN: 978-1-492-03264-9. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling...
Springer, 2019. — 372 p. — ISBN: 978-3-030-21810-2 (eBook). This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the...
O’Reilly Media, 2023. — 466 p. — ISBN-13: 978-1-492-09651-1. The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize...
O’Reilly Media, 2020. — 168 p. — (Early Release). Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools...
O’Reilly Media, Inc., 2020. — 366 p. — ISBN: 978-1-492-05319-4. Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques...
Springer, 2012. — 605 p. First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings. This book constitutes the refereed proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2012, held in Cairo, Egypt, in December 2012. The 58 full papers presented were carefully reviewed and selected...
Springer, 2021. — 214 p. — (Lecture Notes in Mathematics 2293). — ISBN 978-3-030-77561-2. от геометрии к физике и машинному обучению The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to...
Walter de Gruyter GmbH, 2021. — 216 p. — ISBN 978-3-11-070248-4. Machine learning (ML) is a part of computerized reasoning which comprises algorithms and artificial neural networks and displays qualities firmly connected with human insight. The book focuses on the applications of ML for sustainable development. This book provides an understanding of sustainable development and...
Amazon.com Services LLC, 2020. — 51 p. — ISBN: 979-8633574968. Are you prepared for the inevitable AI revolution? How can you use it in your current role as a business leader? Artificial intelligence has become the center of strategic decision making for organizations. It disrupts the way industries function – from sales and marketing to finance and HR, companies are betting on...
Sonar Publishing, 2023. — 222 p. — ISBN-13: 979-8867285340. "Python for Machine Learning: From Fundamentals to Real-World Applications" is your comprehensive roadmap to mastering Machine Learning with Python. Whether you're a beginner looking to enter the exciting world of Data Science or an experienced programmer aiming to delve deeper into Machine Learning, this book provides...
Packt Publishing, 2018. — 350 p. — ISBN: 1788996402. Power up your C# and .NET applications with exciting machine learning models and modular projects Key Features Produce classification, regression, association and clustering models Expand your understanding of machine learning and C# Get the grips of C# packages such as Accord.net, LiveCharts, Deedle Book Description Machine...
Packt Publishing, 2016. — 298 p. — ISBN: 978-1-78588-660-7. Explore the web and make smarter predictions using Python Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps...
2nd edition. — Packt Publishing, 2020. — 821 p. — ISBN 9781839217715. Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin...
Springer Singapore, 2023. — 329 p. — (Machine Learning: Foundations, Methodologies, and Applications) — eBook ISBN: 978-981-19-7584-4. Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics...
Packt, 2019. — 482 p. — ISBN: 1789952298. Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key Features Learn how to select the most suitable Python library to solve your problem Compare...
Wiley-Scrivener, 2024. — 327 p. — (Серия: Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN 978-1-394-23392-2. Метаэвристика для машинного обучения: алгоритмы и приложения The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and...
Gistrup: River Publishers, 2023. — 251 p. This book provides various insights into Machine Learning techniques in healthcare system data and its analysis. Recent technological advancements in the healthcare system represent cutting-edge innovations and global research successes in performance modelling, analysis, and applications. The extensive use of machine learning in...
2nd Edition. — Packt Publishing, 2019. — 326 p. — ISBN: 978-1-78899-459-0. Bring magic to your mobile apps using TensorFlow Lite and Core ML Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with...
2nd Edition. — Apress Media, LLC,, 2024. — 676 p. — ISBN: 978-1-4842-9801-5. This new and updated edition takes you through the details of Machine Learning to give you an understanding of cognitive computing, IoT, Big Data, AI, quantum computing, and more. The book explains how Machine Learning techniques are used to solve fundamental and complex societal and industry...
Packt Publishing Ltd., 2020. — 530 p. — ISBN: 978-1-78995-533-0. Code files only! Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets C++ can make your machine learning models run faster and more efficiently. This handy guide...
3rd edition. — Springer, 2021. — 458 p. — ISBN 978-3-030-81934-7. This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more...
New York: Springer, 2021. — 341 p. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these large-scale data and...
3rd Edition. — Packt Publishing Ltd., 2019. — 488 p. — ISBN: 978-1-78829-586-4. Solve real-world data problems with R and machine learning Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition...
Springer, 2023. — 302 p. — ISBN 978-3-031-35050-4. This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of...
Cham: Springer International Publishing, 2019. — 273 p. — ISBN: 978-3-319-98566-4. This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also...
BPB Publications, 2023. — 271 p. Learn how to use AutoML to leverage Machine Learning for solving business problems. Key Features - Get familiar with the common machine learning problems and understand how to solve them. - Understand the importance of different types of data and how to work with them effectively. - Learn how to use machine learning and AutoML tools to solve...
Springer, 2020. — 285 p. — ISBN: 978-981-15-2910-8 (eBook). This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is...
Scientific Books, 2024. — 126 p. In this book we will develop Machine Learning techniques related to non-linear regression. More specifically, we will go deeper into non-linear multiple regression models with all their identification, estimation and diagnosis problems. Special emphasis is placed on generalised linear models and all types of derived non-linear models: Logit...
Springer, 2023. — 131 p. — (Springer Tracts in Advanced Robotics 156). — ISBN 978-3-031-37831-7. Индуктивные предубеждения в машинном обучении для робототехники и управления One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In...
CRC Press. — 2025. — 361p. — (Chapman & Hall/CRC Artificial Intelligence and Robotics Series). — ISBN: 978-1-032-87013-7. Энергоэффективность и надежность передовых архитектур машинного обучения: межуровневый подход Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient...
Abiprod Pty, 2018. — 125 p. — ISBN: 1718095031. Are you excited about Artificial Intelligence and want to get started?Are you excited about Machine Learning and want to learn how to implement in Python? The book below is the answer. Given the large amounts of data we use everyday; whether it is in the web, supermarkets, social media etc. analysis of data has become integral to...
No Starch Press, 2024. — 272 p. — ISBN-13: 978-1718502109. Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the...
Wiley & Sons, Inc., 2020. — 336 p. — ISBN: 978-1-119-60290-3 (ebk). Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence...
Springer Cham, 2025. — 132 p. — (Synthesis Lectures on Engineering, Science, and Technology). — eBook ISBN 978-3-031-82010-6. Comprehensive guide to MLOps. Covers all aspects of the MLOps process. No prior knowledge of MLOps is required. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable...
Leanpub, 2023. — 216 p. Master Machine Learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your Machine Learning applications. Machine Learning is transforming fields from healthcare diagnostics to climate change predictions through their predictive performance. However, these complex Machine...
Leanpub, 2023-02-14. — 101 р. This book teaches you how to quantify the uncertainty of machine learning models with conformal prediction in Python. Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification. Summary A prerequisite for trust in Machine Learning is uncertainty...
Amazon Digital Services LLC, 2019. — 46 p. Order Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science, NumPy, Scikit Learn, Pandas and Tensorflow now to learn all the basic concepts you need to know about machine learning and Python. The purpose of...
New York: Routledge, 2023. — 364 p. Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich,...
Apress, 2021. — 192 p. — ISBN 1484265424, 9781484265420. Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application...
Manning Publications Co, 2021. — 493 p. — ISBN 9781617295645. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs....
Apress Media LLC., 2020. — 570 p. — ISBN13: (electronic): 978-1-4842-5174-4. Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most...
Amazon Digital Services LLC, 2018. — 129 p. — ASIN B07F2NYDTH. Smart homes, self-driving cars, Siri, Alexa - some typical examples of how machine learning and artificial intelligence have become part of our daily life. Wouldn't it be cool to understand the concepts behind these complex topics? This book teaches you how to integrate machine learning into your apps. We're going...
Springer, 2021. — 170 p. — ISBN 978-3-030-74041-2. This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing...
O’Reilly, 2019. — 337 p. — ISBN: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where...
O’Reilly Media, 2020. — 358 p. — ISBN13: 978-3-96088-877-2. Ein Großteil der weltweit verfügbaren Daten ist ungelabelt. Auf diese nicht klassifizierten Daten lassen sich die Techniken des Supervised Learning, die im Machine Learning viel genutzt werden, nicht anwenden. Dagegen kann das Unsupervised Learning - auch unüberwachtes Lernen genannt - für ungelabelte Datensätze...
Birkhäuser Cham, 2022. — 152 p. — ISBN 978-3-030-98316-1 This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization...
Packt, 2022. — 440 p. — ISBN 1801072167, 9781801072168. Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building,...
Apress Media LLC, 2024. — 984 p. — ISBN-13: 979-8-8688-1023-7. This book is the first comprehensive guide to the intersection of data governance and Machine Learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data is central to their success, which gives ML data governance unprecedented significance. However, adapting data...
O’Reilly Media, Inc., 2023. — 376 р. — ISBN 978-1-098-12027-6. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce...
Xlibris US, 2021. — 556 р. This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised Machine Learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector...
Apress, 2020. — 355 p. — ISBN: 1484256689. Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation...
Packt Publishing, 2021. — 270 p. — ISBN 978-1800567887. Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models...
Springer, 2022. — 464 p. — ISBN 978-3-030-96756-7 This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML...
Packt Publishing, 2019. — 502 p. — ISBN: 978-1838556334 (paperback). Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. Key Features Study supervised learning algorithms by using real-world datasets Fine tune optimal...
Leanpub, Sebastian Raschka, 2023. — 231 p. Have you recently completed a Machine Learning or Deep Learning course and wondered what to learn next? With 30 questions and answers on key concepts in Machine Learning and AI, this book provides bite-sized bits of knowledge for your journey to becoming a Machine Learning expert. Expand Your Machine Learning Knowledge Machine Learning...
IGI Global, 2017 - 129 p. Software development and design is an intricate and complex process that requires a multitude of steps to ultimately create a quality product. One crucial aspect of this process is minimizing potential errors through software fault prediction. Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities is an innovative...
New York: amazon.com Services LLC, 2021. — 412 p. — ISBN B08SCQ2Y75. Machine learning can be broadly classified into supervised and unsupervised learning. By definition, the term supervised means that the “machine” (the system) learns with the help of something—typically a labeled training data. Training data (or a dataset ) is the basis on which the system learns to infer. An...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
USA: IGI Global, 2024. — 557 p. — (Advances in Information Security, Privacy, and Ethics (AISPE) Series). — ISBN 9798369341599. Машинное обучение и криптографические решения для защиты данных и сетевой безопасности As the Editors of this comprehensive reference book, Machine Learning and Cryptographic Solutions for Data Protection and Network Security, we are delighted to...
Packt Publishing, 2019. — 252 p. — ISBN 1838828974, 9781838828974. Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step...
Sanshodhana, 3016. - 39 p. - ISBN: 1520269277 This book is an introduction to theory and experiments in Machine Learning. The book has simple examples to explain what is possible using Machine Learning, this first edition has cut down on the mathematical aspect and concentrates on logical connections in explaining Machine Learning through experiments.
O’Reilly Media, Inc., 2024. — 329 p. — ISBN: 978-1-492-09452-4. Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In...
Manning Publications Co., 2019. — 280 p. — ISBN: 978-1617293702. MEAP Version 7 It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine...
Manning Publications Co., 2019. — 280 p. — ISBN: 978-1617293702. MEAP Version 7 It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine...
Springer, 2023. — 175 p. — (Engineering Optimization: Methods and Applications). — ISBN 978-981-99-7455-9. This book aims to provide a collection of state-of-the-art scientific and technical research papers related to Machine Learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering...
2nd Edition. — Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce. — Wiley, 2023. — 611 p. — ISBN 978-1119903833. MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
Packt, 2020. — 246 p. — ISBN: 9781838646486. Build secure private blockchain networks to handle mission-critical security challenges such as denial-of-service attacks, user wallets, and pool mining attacks Key Features Explore blockchain concepts such as cryptography, consensus algorithms, and security assumptions Architect network security for mission-critical decentralized...
Apress, 2020. - 258p. - ISBN: 9781484262214 Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load...
Manning Publications, 2018. — 256 p. — ISBN: 978-1-6172-9333-7. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
Springer, 2023. — 249 p. Master the art of Machine Learning and Data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the...
2nd ed. — Apress, 2019. — 469 p. — ISBN: 1484249461. Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts...
Emerald Publishing, 2021. — 479 p. — ISBN 978-1-80043-882-8. Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer...
Springer, 2023. — 254 p. This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. The book starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from edge computing platforms. Using practical labs, each...
Independently published, 2023. — 83 p. Machine Learning is a fascinating field that has the potential to revolutionize the way we live and work. However, it can be overwhelming for beginners to understand the complex algorithms and concepts involved. This book is designed to make Machine Learning accessible and easy to understand for anyone who wants to learn. The algorithms...
O’Reilly Media, 2020. — 432 p. — ISBN: 978-1-492-07305-5. Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised,...
W. W. Norton & Company, 2024. — 176 p. — (A Norton Short). In the industrial age, automation came for the shoemaker and the seamstress. Today, it has come for the writer, physician, programmer, and attorney. Literary Theory for Robots reveals the hidden history of modern machine intelligence, taking readers on a spellbinding journey from medieval Arabic philosophy to visions of...
Independently published, 2020. — 302 p. — ISBN B08NZW37N6. 4 Books in 1! Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading...
Oliver Theobald, 2017. — 52 p. The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the...
Packt, 2019. - 346p. - ISBN: 9781789614671 Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and...
Leanpub, 2020. — 295 p.— ISBN B084FXKCS8. This version was published on 2020-07-13 This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine...
Reactive Publishing, December 28, 2023. — 371 p. Dive into the world of Artificial Intelligence with "Unsupervised Machine Learning with Python," the essential guide forprofessionals eager to master the most sophisticated analysis skills and unlock new dimensions of data interpretation. Building on the knowledge foundation of those who have already ventured into the realm of...
Packt, 2023. — 905 p. Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working...
De Gruyter, 2024. — 210 р. — ISBN: 978-3-11-128981-6. This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory...
Apress Media LLC., 2020. — 392 p. — ISBN13: (electronic): 978-1-4842-6156-9. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as...
BPB Publications, 2023. — 302 р. — ISBN 978-93-89898-27-9. A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques. Key Features - Understand the core concepts and algorithms of Machine Learning. - Get started with your Machine Learning career with this easy-to-understand guide. - Discover different Machine Learning...
Morgan & Claypool, 2018. — 169 p. — ISBN: 9781681733968 (ebook). Synthesis Lectures on Artificial Intelligence and Machine Learning The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language,...
Birmingham: Packt Publishing, 2022. — 541 p. — ISBN 1803241675. Explore supercharged machine learning techniques to take care of your data laundry loads. Key Features Learn how to prepare data for machine learning processes. Understand which algorithms are based on prediction objectives and the properties of the data. Explore how to interpret and evaluate the results from...
Independently published, 2020. — 335 p.— ISBN B08RCSJ39K. Mastering Deep Learning with Python: 2 Manuscripts This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and...
Springer International Publishing AG, 2018. — 482 p. — (Human–Computer Interaction Series). — eBook ISBN 978-3-319-90403-0. With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of...
Academic Press/Elsevier, 2023. — 404 р. — ISBN 978-0-323-89931-4. Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve....
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
Автор, 2023. — 110 с. Практическое руководство, предназначенное для всех, кто хочет войти в мир машинного обучения и освоить его основы. Авторы книги предлагают читателям увлекательное путешествие в эту захватывающую область, начиная с основных концепций и принципов машинного обучения и заканчивая практическими навыками построения и обучения моделей. Внутри книги читатели...
Автор, 2023. — 110 с. Практическое руководство, предназначенное для всех, кто хочет войти в мир машинного обучения и освоить его основы. Авторы книги предлагают читателям увлекательное путешествие в эту захватывающую область, начиная с основных концепций и принципов машинного обучения и заканчивая практическими навыками построения и обучения моделей. Внутри книги читатели...
Автор, 2023. — 65 с. — ISBN 978-5-0060-1962-1. Краткий гайд для новичков по машинному и глубокому обучению с разбором кода. Здесь вы найдете необходимый минимум по предмету, истолкованный языком, понятным школьнику. Некоторые разделы написаны с помощью chatGPT. По прочтении вы избавитесь от страха перед технологией и освоите базовый инструментарий подготовки данных, их загрузке...
Монография. — М.: Наука, 2021. — 128 с. — ISBN 978‑5‑02‑040908‑8. Монография написана на стыке двух больших научных направлений: математического программирования и машинного обучения. Авторами предлагается подход к решению ряда практических задач регрессии и классификации на основе математического программирования. Речь идет о задачах, решаемых медицинскими работниками,...
Монография. — Ульяновск: УлГТУ, 2023. — 183 с. В монографии авторы изложили методы и алгоритмы конструирования признаков из контекста динамики показателей процессов. Рассмотрены подходы к получению, синтезу, подготовке, анализу таких данных, подходы к интерпретации результатов машинного обучения. Теоретические разработки поддержаны практическими примерами, иллюстрирующими...
Springer, 2020. — 506 p. — ISBN: 978-3-030-40344-7 (eBook). This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced...
IGI Global, 2023. — 385 p. — ISBN-13: 978-1668456446. In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address...
CRC Press, 2023. — 378 p. — ISBN 978-1-003-17025-9. Стратегии после усадки в статистическом и машинном обучении для данных высокой размерности This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in Data Science. It combines statistical learning and Machine Learning techniques in a unique and optimal way....
Independently published, 2021. — 171 p. — ISBN 979-8724724272. Machine Learning is rapidly changing the world and it is the way of the Future of Human Technology. Art, information, processes, calculations, emotions will be rapidly learned and discover from machines. Do you want to learn more about the world of Machine Learning and its Applications? Would you like to improve and...
O’Reilly, 2020. — 150 p. — ISBN: 978-1-492-04510-6. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn...
O’Reilly, 2020. — 150 p. — ISBN: 978-1-492-04510-6. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn...
O’Reilly, 2020. — 260 p. — ISBN: 978-1-492-04510-6. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn...
Independently published, 2024. — 331 p. — ASIN : B0CW1C5S62. Many believe that a machine learning model, once trained, can act autonomously. This misconception has hindered innovation in ML/AI for far too long. In reality, ML models require integration within a comprehensive system encompassing inputs, processing, and outputs. My new book, "Applied Machine Learning: A Practical...
Apress Media LLC., 2024. — 478 р. — ISBN-13: 979-8-8688-0354-3. This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various...
Manning Publications, 2025. — 376 p. — ISBN-13: 978-1633438750. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside,...
BPB Publications, 2024. — 392 p. — ISBN 978-93-55519-818. Build high-impact ML/AI solutions by optimizing each step Key Features Build and fine-tune models for maximum performance. Practical tips to make your own state-of-the-art AI/ML models. ML/AI problem solving tips with multiple case studies to tackle real-world challenges. Description This book approaches data science...
BPB Publications, 2023. — 353 p. — ISBN 13: 978-9355518446. Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers. Key Features Gain a comprehensive understanding of TinyML's core concepts. Learn how to design your own TinyML applications from the ground up. Explore cutting-edge models, hardware, and software...
Khaled Bayoudh, 2017. — 166 p. There are a growing number of people who are seeking to understand the main concepts of Machine/Deep Learning and what powers them up. And if you are of these people, then this book is for you! This book discusses the Machine/Deep Learning algorithms, methods, concepts, functions and code that make Deep Neural Networks such as Convolutional Neural...
Wiley, 2014. — 408 p. — ISBN: 1118889061. Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML...
Gulf Professional Publishing, 2021. — 461 p. — ISBN 978-0128219294. Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves...
O’Reilly Media, 2016. — 82 p. — ISBN: 978-1-491-96297-8. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding...
Academic Press / Elsevier, 2023. — 348 p. — ISBN 978-0-323-96098-4. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and...
2nd Edition. — BPB Publications, 2024. — 472 р. — ASIN: B0CLL37MFL. The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in Machine Learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection,...
CRC Press, 2020. — 488 p. — ISBN-13: 978-1-138-49568-5. Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R,...
Packt Publishing, 2019. — 375 p. — ISBN: 978-1-78934-827-9. Code files only! Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised...
2nd Edition. — Packt Publishing, 2018. — 604 p. — ISBN: 978-1-78934-799-9. Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Machine learning has gained tremendous popularity for its powerful and fast...
Springer, 2020. - 500p. - ISBN: 9783030457778 This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in...
Packt Publishing, 2015. — 190 p. — ISBN: 978-1-78439-908-5. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine...
Independently published, 2018. — 79 р. Do you want to impress the processes that you are working on? Do you want to make your machines more intelligent? If your answer to any of those questions is yes, then you have come to the right place. This book is a sequel to the book titled 'Machine Learning: A Step-by-Step guide.' In the first book, you gathered information on what...
O’Reilly Media, 2022. — 333 p. — ISBN-13: 978-1-492-08992-6. Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train...
O’Reilly Media, 2022. — 333 p. — ISBN-13: 978-1-492-08992-6. Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train...
Chapman and Hall/CRC, 2022. — 154 p. — ISBN 9781003267003. This book introduces the reader to Artificial Intelligence and its importance to our future. Campbell uses behavioural psychology, explores technology, economics, real-life and historical examples to predict five future scenarios with AI. Illustrating through speculative fiction, she describes possible futures after AI...
Mercury Learning and Information, 2024. — 169 p. — ISBN-13: 978-150152-248-2. This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an...
Mercury Learning & Information, 2020. — 363 p. — ISBN: 978-1-68392-495-1. This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is...
Amazon.com Services LLC., 2020. — 102 p. Are you looking for a guide that will teach you all you need to know about machine learning? Are you looking for a way to learn how to write algorithms from scratch? Then read on… Artificial intelligence is a common part of our lives, and we use it daily. Machine learning is one application of artificial intelligence and is where...
Apress, 2019. — 248 p. — ISBN13: (electronic): 978-1-4842-5107-2. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in...
Springer Cham, 2023. — 391 p. — (Statistics and Computing) — eBook ISBN: 978-3-031-41337-7. Presents the fundamental notions of supervised machine learning Provides a balance between the theory and applications of machine learning using Python, R, and Stata Fosters an understanding and awareness of machine learning methods over different software platforms This book presents...
Scrivener Publishing LLC., Wiley, 2022. — 470 p. — (Advances in Learning Analytics for Intelligent Cloud-IoT Systems). — ISBN 978-1-119-76225-6. Методы машинного обучения и аналитика для облачной безопасности This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine...
Independently published, 2021. — 225 p. — ASIN B09BNYDH5F. Have you always been curious about machine learning but do not know where to start. Or perhaps your new job requires you to learn machine learning but you are overwhelmed with all the information online. What is machine learning? What is Scikit-Learn? What does the fit() method that you see on so many online tutorials...
Manning Publications, 2023. — 335 p. — ISBN: 978-1617298042. Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your Machine Learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation...
Manning Publications, 2023. — 335 p. — ISBN: 978-1617298042. Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your Machine Learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation...
Bentham Science Publishers, 2022. — 360 p. — ISBN 978-1-68108-940-9. Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include...
Academic Press/Elsevier, 2024. — 520 p. Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated...
Wiley, STE Ltd., 2022. — 255 p. — (Computer Science, Operational Research and Decision). — ISBN 978-1-78945-071-2. Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the...
Bentham Science Publishers, 2023. — 139 p. — eISBN: 978-981-5124-42-2. Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications,...
Springer, 2021. — 173 p. — ISBN 978-3-030-70678-4. This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The...
Independently published, 2020. — 210 p. — ISBN: 979-8611346952. Would you like to learn how to use Python to generate machine learning models but you think it would be too difficult? Or perhaps you want to automate simple things with your computer but you don’t know how to do it? Here’s the deal… As a beginner you might think that programming is complex… Learning artificial...
2nd Edition. — O’Reilly Media, Inc., 2024. — 556 p. — ISBN 978-1-098-15601-5. Производственные системы машинного обучения: инженерные модели и конвейеры машинного обучения. Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from...
Academic Press, 2023. — 326 p. — eBook ISBN: 9780128229057. Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple...
Springer, 2021. — 162 p. — ISBN 978-3-030-83212-4. This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting;...
Orange Education Pvt Ltd, AVA, 2024. — 714 p. — ISBN 978-81-97651-20-5. Ultimate MLOps для моделей машинного обучения: используйте реальные практические примеры для эффективного создания, развертывания и масштабирования конвейеров машинного обучения с помощью MLOps The only MLOps guide you'll ever need. Key Features - Acquire a comprehensive understanding of the entire MLOps...
BPB Publications, 2023. — 150 р. — ISBN-13: 978-93-55511-133. A guide to understand the basics of Machine Learning and its application in the field of education. Key Features: - Create a more efficient and effective learning environment that meets the needs of all students. - Learn how to use the profound Machine learning advancements in the field of education. - Understand how...
Packt Publishing, 2020. — 348 p. — ISBN: 978-1-83882-441-9. Code files only! Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With...
Leanpub, 2023. — 192 р. This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on...
Data Sciences, 2017. — 136 p. — ISBN: 1985670968. The overall aim of this book is to help you understand the application of machine learning techniques with Python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what...
Data Sciences, 2017. — 136 p. — ISBN: 1985670968. The overall aim of this book is to help you understand the application of machine learning techniques with Python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what...
Springer, 2023. — 253 p. — (Data Science, Machine Intelligence, and Law 02). — ISBN 978-3-031-19666-9. This book gives a comprehensive overview of the state of Artificial Intelligence (AI), especially Machine Learning (ML) applications in public service delivery in Estonia, discussing the manifold ethical and legal issues that arise under both European and Estonian law. Final...
Springer, 2023. — 231 p. — (Computational Intelligence Methods and Applications). — ISBN 978-981-19-3887-0. Метаэвристика для машинного обучения: новые достижения и инструменты Using metaheuristics to enhance Machine Learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule...
2nd edition. — Boca Raton: CRC Press, 2025. — 242 p. A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of Machine Learning concepts from basic principles and illustrates every concept using examples in both Python and MatLAB, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter...
Apress, 2024. — 478 p. — ISBN 9798868803536, ASIN B0CYNRV8BK. This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various...
Springer Cham, 2023. — 267 p. — eBook ISBN: 978-3-031-39477-5. Tackles the 'why' questions of data science and deep learning Interdisciplinary approach to model engineering Information measurements for MLOps, Data drift, bias This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the...
2nd Edition. — O’Reilly Media, Inc., 2023. — 410 p. — ISBN-13 9781098135720. This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems all the way from loading...
2nd Edition. — O’Reilly Media, Inc., 2023. — 410 p. — ISBN-13 9781098135720. This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems all the way from loading...
Leanpub, 2022-08-24. — 52 р. Learn how to implement various feature selection methods in a few lines of code utilizing the open-source Python library Feature-engine. Feature-engine is an open-source Python library for feature engineering and feature selection. It uses Pandas and Scikit-learn under the hood to engineer and select feature subsets. Feature selection is the process...
BPB Publications, 2021. — 218 p. — ISBN: 978-93-90684-700. Utilize Python and IBM Watson to put real-life use cases into production. Key Features Use of popular Python packages for building Machine Learning solutions from scratch. Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. Expert-led best practices to put...
Springer Singapore, 2024. — 304 p. — eBook ISBN 978-981-97-3954-7. Explains basic theories without over explanation for readers to build machine learning models Guides readers step by step to use KNIME to practice machine learning Provides examples from Kaggle competitions in various fields This book guides professionals and students from various backgrounds to use machine...
Independently published, 2019. — 146 p. — ISBN-13 978-1089401513. What is machine learning? How machine learning works? Should I use a machine learning model or another approach to solve my problem? How do I implement machine learning to my problem? What are the machine learning methods I can use for my problem? How do I know my machine learning model is efficient? Are you...
EGEA Spa, 2020. — 204 p. — ISBN: 978-88-99902-65-0. This book gives the fundamental principles for developing Machine Learning applications with Python. Introduction to Machine Learning Linear Models for Machine Learning Beyond Linearity: Ensemble Methods for ML An Introduction to Modern ML Techniques Appendices A crash course in Python Mathematics behind the skip-gram model
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko. — O’Reilly Media, Inc., 2020-10-12. — 264 p. — ISBN: 978-1-492-05012-4. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data...
Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko. — O’Reilly Media, Inc., 2020-10-12. — 264 p. — ISBN: 978-1-492-05012-4. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data...
Morgan Kaufmann/Elsevier, 2024. — 410 p. — ISBN-13: 978-0-443-21857-6. Synthetic Data and Generative AI covers the foundations of Machine Learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression...
Morgan Kaufmann/Elsevier, 2024. — 410 p. — ISBN-13: 978-0-443-21857-6. Synthetic Data and Generative AI covers the foundations of Machine Learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression...
Manning Publications, 2020. — 319 p. In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through...
Springer, 2024. — 180 p. — (Machine Learning: Foundations, Methodologies, and Applications). — ISBN 978-981-97-0687-7. Today, Machine Learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may...
Springer, 2022. — 78 p. — (SpringerBriefs in Computer Science). — ISBN 978-981-19-4873-2. Использование машинного обучения в программно-определяемых сетях Emerging Machine Learning (ML) techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of...
CRC Press, 2019. — 364 p. — ISBN 13 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
CRC Press, 2019. — 364 p. — ISBN 13: 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
CRC Press, 2019. — 364 p. — ISBN 13 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
Springer, 2019. — 372 p. — ISBN: 978-3-030-21810-2 (eBook). This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the...
O’Reilly Media, 2023. — 466 p. — ISBN-13: 978-1-492-09651-1. The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize...
Independently published, 2024. — 107 р. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
Independently published, 2024. — 107 р. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
O’Reilly Media, 2020. — 366 p. — ISBN: 1492053198. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools...
O’Reilly Media, 2019. — 320 p. — ISBN: 978-1-492-04754-4. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal...
O’Reilly Media, 2019. — 320 p. — ISBN: 978-1-492-04754-4. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal...
Packt Publishing, 2016. — 278 p. — ISBN: 978-1-78439-863-7. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum...
Leanpub, 2020. — 505 p. — ISBN NA. This version was published on 2020-10-09 Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11. Important: I will not be updating this book to the new features introduced with iOS 15 and...
Leanpub, 2020. — 505 p. — ISBN NA. This version was published on 2020-10-09 Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11. Important: I will not be updating this book to the new features introduced with iOS 15 and...
Leanpub, 2020. — 505 p. — ISBN NA. Code Files Only! This version was published on 2020-10-09 Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11. Important: I will not be updating this book to the new features introduced...
CRC Press, 2023. — 336 p. — ISBN: 978-1003187158. Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and...
Amazon.com Services LLC, 2020. — 51 p. — ISBN: 979-8633574968. Are you prepared for the inevitable AI revolution? How can you use it in your current role as a business leader? Artificial intelligence has become the center of strategic decision making for organizations. It disrupts the way industries function – from sales and marketing to finance and HR, companies are betting on...
Sonar Publishing, 2023. — 222 p. — ISBN-13: 979-8867285340. "Python for Machine Learning: From Fundamentals to Real-World Applications" is your comprehensive roadmap to mastering Machine Learning with Python. Whether you're a beginner looking to enter the exciting world of Data Science or an experienced programmer aiming to delve deeper into Machine Learning, this book provides...
Packt Publishing, 2019. — 563 p. — ISBN: 978-1-78934-634-3. Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and...
Amazon Digital Services LLC, 2019. — 135 р. — ASIN B07NRVL21R. There are different processes that are included within the meaning of the term "learning." If you refer to a dictionary and look up the meaning of learning, then you will come across different phrases like "to gain knowledge, understanding of or skill, through study, experience or instruction," and "the change in...
Amazon Digital Services LLC, 2019. — 135 р. — ASIN B07NRVL21R. There are different processes that are included within the meaning of the term "learning." If you refer to a dictionary and look up the meaning of learning, then you will come across different phrases like "to gain knowledge, understanding of or skill, through study, experience or instruction," and "the change in...
2nd Revised Edition. — Cambridge University Press, 2025. — 426 p. — ISBN-13: 978-1-316-51886-1. As Machine Learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers...
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu. — Packt Publishing, 2019. — 416 p. —ISBN: 978-1-83855-013-4. Code files only! Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems With huge amounts of data being generated every moment, businesses need applications that...
Packt Publishing, 2019. — 380 p. — ISBN: 978-1-78995-492-0. Explore the exciting world of machine learning with the fastest growing technology in the world Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply...
Packt Publishing, 2019. — 403 p. — ISBN: 978-1-78995-492-0. Code files only! Explore the exciting world of machine learning with the fastest growing technology in the world Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you...
2nd Edition. — Springer, 2023. — 279 p. The new edition of this popular professional book on Artificial Intelligence (ML) and Machine Learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective,...
Packt Publishing, 2016. — 304 p. — ASIN B01BSTEDKY. Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range...
CRC Press, 2023. — 298 p. — ISBN 978-1003002611. Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and...
Packt Publishing, 2018. — 367 p. — ISBN: 978-1-78899-824-6. A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added...
O’Reilly Media, Inc. 2023. — 250 p. — ISBN 978-1-492-09767-9. Whether based on academic theories or discovered empirically by humans and machines, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability...
Independently published, 2021. — 674 p. — ISBN B08V4VZSCH. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine...
IGI Global, 2020. — 324 p. — ISBN 9781799801832. Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and...
O’Reilly Media, 2017. — 99 р. — ISBN: 978-1-491-92406-8. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
Packt Publishing Ltd., 2020. — 530 p. — ISBN: 978-1-78995-533-0. Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn...
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
CRC Press, 2024. — 362 p. — ISBN 978-1-032-47162-4, 978-1-032-47163-1, 978-1-003-38485-4. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become...
Apress, 2020. — 146 p. — ISBN13: 978-1-4842-5939-9. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best...
Artech House, 2025. — 329 p. — ISBN-13: 978-1-68569-033-5. Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification...
Manning Publications, 2023. — 362 p. — (Final Release). — ISBN 978-1617299520. Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Summary In Evolutionary Deep Learning you will...
Manning Publications, 2023. — 362 p. — (Final Release). — ISBN 978-1617299520. Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Summary In Evolutionary Deep Learning you will...
Packt, 2019. - 458p. - 978-1788295864 3rd.ed. Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by...
John Wiley & Sons, Inc., 2019. — 307 p. — ISBN: 978-1-119-54567-5. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on...
Springer, 2022. — 109 p. — (Wireless Networks). — ISBN 978-3-031-16374-6. This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing...
BPB Publications, 2023. — 250 p. — ISBN 978-93-5551-784-5. Learn how to use AutoML to leverage Machine Learning for solving business problems. Key Features - Get familiar with the common machine learning problems and understand how to solve them. - Understand the importance of different types of data and how to work with them effectively. - Learn how to use machine learning and...
Springer, 2020. — 285 p. — ISBN: 978-981-15-2910-8 (eBook). This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is...
Bentham Books, 2022. — 240 p. — ISBN 978-981-5079-19-7. This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it...
Wiley-IEEE, 2020. — 496 p. — ISBN 978-1119562313(EPUB). A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the...
Orange Education Pvt. Ltd , 2024. — 247 р. — ISBN 8197256373, 978-8197256370, ASIN B0D8L3Q283. Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it...
Springer, 2021. — 349 p. — (Algorithms for Intelligent Systems). — ISBN 978-981-33-4045-9. This book comprises the best deliberations with the theme “Machine Learning Technologies and Applications” in the “International Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2020),” organized by the Department of Computer Science and Engineering, VNR...
Springer, 2022. — 254 p. This book introduces machine learning and its applications in smart environments/cities. At this stage, a comprehensive understanding of smart environment/city applications is critical for supporting future research. This book includes chapters written by researchers from different countries across the globe and identifies critical threads in research...
Packt Publishing, 2021. — 344 p. — ISBN 978-1-80107-812-2. Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key Features Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML Leverage SQL syntax to train, evaluate,...
Packt Publishing, 2021. — 737 p. — ASIN B08PDFXXRL. Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with...
Birmingham: Packt Publishing, 2021. — 312 p. — ISBN 9781800567689. Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning Key Features Get up to speed with AutoML using the platform of your choice, such as OSS, Azure, AWS, or GCP. Eliminate mundane tasks in data engineering and reduce human errors in ML...
Packt Publishing, 2019. — 293 p. — ISBN: 978-1-78934-979-5. Code files only! Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning...
Springer, 2020. - 286 p. - (Algorithms for Intelligent Systems). - ISBN: 9813299894. This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines,...
CRC Press, 2022. — 212 p. — ISBN: 978-0-367-63453-7. The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature,...
Independently published, 2021. — 93 p. — ASIN B0952D2T8C. The Machine Learning process is revolutionizing many sectors in engineering including manufacturing sectors such as the Friction Stir Welding Process. The implementation of machine learning algorithms in Friction Stir Welding process not only reduced the time of the experiment but also reduces the cost of the experiment....
2nd Edition. — Leanpub, 2022-03-04. — 329 р. This book teaches you how to make Machine Learning models more interpretable. Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of Machine Learning. This book is about making Machine Learning models and their...
Leanpub, 2023-02-14. — 101 р. This book teaches you how to quantify the uncertainty of machine learning models with conformal prediction in Python. Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification. Summary A prerequisite for trust in Machine Learning is uncertainty...
Amazon Digital Services LLC, 2018. — 50 р. Excited about computer science, machine learning and programming? This book is the ultimate guide to Python programming for beginners and intermediates! Considering that Python is used by big tech companies like Google, Nokia, IBM, Yahoo!, Pinterest, Disney and many others, it makes a lot of sense to prioritize learning Python if you...
De Gruyter, 2023. — 506 р. — ISBN: 978-3-11-078612-5. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 478 р. — ISBN 978-3-11-078614-9. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 478 р. — ISBN 978-3-11-078614-9. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 364 р. — ISBN 978-3-11-078613-2. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
O’Reilly Media, Inc., 2023. — 279 p. — ISBN 978-1-098-11913-3. Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for...
O’Reilly Media, Inc., 2023. — 279 p. — ISBN 978-1-098-11913-3. Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for...
Springer, 2023. — 101 p. — (SpringerBriefs in Computer Science). This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and...
Leanpub, 2022. — 112 p. Build a career while doing technical wriitng. I earned $300 for my first paid Data Science and Machine Learning article. I get paid between $250 and $500 for each Data Science article I write. In this ebook, I'll show you how you too, can earn while writing about Data Science and Machine Learning. You have been learning about Data Science and Machine...
Leanpub, 2022. — 112 p. Build a career while doing technical wriitng. I earned $300 for my first paid Data Science and Machine Learning article. I get paid between $250 and $500 for each Data Science article I write. In this ebook, I'll show you how you too, can earn while writing about Data Science and Machine Learning. You have been learning about Data Science and Machine...
Wiley-Scrivener, 2024. — 480 p. — ISBN 978-1-394-21411-2. Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As...
Manning Publications, 2020. — 360 p. — ISBN: 978-1617295645. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core...
Independently published, 2024. — 84 p. Do you find yourself unsure of how to apply your existing knowledge to Python? If you are a beginner programmer who wants to learn Python Machine Learning, this book is for you. This book will help you understand how to use Python to apply your existing skills to Machine Learning problems. Machine Learning is a fascinating and powerful...
Amazon Digital Services LLC, 2018. — 54 р. — (Machine Learning for Beginners Book 1). Welcome to the world of machine learning! Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core...
Amazon Digital Services LLC, 2018. — 54 р. — (Machine Learning for Beginners Book 1). Welcome to the world of machine learning! Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core...
Apress Media LLC., 2020. — 570 p. — ISBN13: (electronic): 978-1-4842-5174-4. Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most...
Manning Publications, 2022. — 358 р. — ISBN: 978-1617299797. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This...
Springer, 2021. — 178 p. — (Studies in Big Data, 87). — ISBN 978-981-33-6517-9. This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning,...
Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli. — Apress Media LLC., 2020. — 320 p. — ISBN13: (electronic): 978-1-4842-6094-4. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow...
2nd edition, Apress, 2021. — 428 p. — ISBN 148426536X, 9781484265369. This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine...
O’Reilly Media, 2021-12-10. — 362 p. — ISBN-13: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world’s data is unlabeled, conventional supervised learning cannot be applied;...
O’Reilly Media, 2021-12-10. — 362 p. — ISBN-13: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world’s data is unlabeled, conventional supervised learning cannot be applied;...
O’Reilly Media, 2020. — 358 p. — ISBN13: 978-3-96088-878-9. Ein Großteil der weltweit verfügbaren Daten ist ungelabelt. Auf diese nicht klassifizierten Daten lassen sich die Techniken des Supervised Learning, die im Machine Learning viel genutzt werden, nicht anwenden. Dagegen kann das Unsupervised Learning - auch unüberwachtes Lernen genannt - für ungelabelte Datensätze...
Code Planet, 2025. — 270 p. Machine Learning with Python in this all-in-one guide designed for beginners and experienced developers alike! Whether you're diving into supervised and unsupervised learning, exploring neural networks, or mastering real-world applications, this book provides step-by-step explanations, hands-on examples, and expert insights. Machine Learning (ML) is...
O’Reilly Media, 2022. — 300 p. — ISBN-13: 978-1-098-10682-9. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you’re looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you....
O’Reilly Media, 2023. — 271 p. — ISBN-13: 978-1-098-11722-1 Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use...
Apress, 2021. — 127 p. — ISBN 978-1912807130. Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an...
San Francisco: No Starch Press, 2024. — 263 p. — ISBN 1718503768. Если вы готовы выйти за рамки вводных концепций и углубиться в машинное обучение, глубокое обучение и искусственный интеллект, формат вопросов и ответов сделает все быстро и просто для вас, без особых хлопот. Прямой, недвусмысленный подход этой книги, основанный на вопросах, которые часто задает автор Себастьян...
Packt Publishing, 2016. — 1547 p. — ISBN: 978-1-78712-857-6. Leverage benefits of machine learning techniques using Python Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine...
CRC Press, 2022. — 211 p. — ISBN 978-1-032-07401-6. Physics of Data Science and Machine Learning links fundamental concepts of physics to Data Science, Machine Learning and Artificial Intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Packt Publishing, 2017. — conv 1276 p. — ASIN B076CRXB76. Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Final version! Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of...
Manning Publications, 2020. — 528 p. — ISBN: 978-1617296574. Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools...
Lioncrest Publishing, 2021. — 222 p. — ISBN 978-1-5445-1882-4. How can you successfully deploy AI? When AI works, it’s nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren’t...
2nd Edition. — World Scientific Publishing Co. Pte. Ltd., 2019. — 302 p. — ISBN 978-981-120-195-0. Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition) (Series in Machine Perception and Artificial Intelligence) 2nd Edition This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories,...
2nd Edition. — Packt Publishing Ltd., 2020. — 579 p. — ISBN: 978-1-83921-153-9. Code files only! Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples. Artificial intelligence (AI) has the potential to replicate humans in every field. Artificial Intelligence by Example serves as a starting point for you to understand...
BPB Publications, 2021. — 866 р. This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms...
Sanshodhana, 3016. - 39 p. - ISBN: 1520269277 This book is an introduction to theory and experiments in Machine Learning. The book has simple examples to explain what is possible using Machine Learning, this first edition has cut down on the mathematical aspect and concentrates on logical connections in explaining Machine Learning through experiments.
Sanshodhana, 3016. - 39 p. - ISBN: 1520269277 This book is an introduction to theory and experiments in Machine Learning. The book has simple examples to explain what is possible using Machine Learning, this first edition has cut down on the mathematical aspect and concentrates on logical connections in explaining Machine Learning through experiments.
Packt Publishing, 2019. — 285 p. — ISBN: 978-1-83882-173-9. Code files only! Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use...
Orange Education Pvt Ltd, AVA, 2024. — 411 p. — ISBN 978-81-97223-94-5. Максимальное машинное обучение с Scikit-Learn: раскройте возможности Scikit-Learn и Python для создания передовых приложений прогнозного моделирования и более глубокого понимания машинного обучения Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. “Ultimate...
Independently published, 2022. — 240 p. — ISBN-10: B09HKHPT9M. Why was this book written? Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, AI Engineering being associated with machine learning. Our aim in this book is to demystify these concepts and provide...
2nd Edition. — Balige Publishing, 2023. — 355 p. — ISBN-10: B0C7K3N8ZR. Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it...
2nd Edition. — Balige Publishing, 2023. — 355 p. — ISBN-10: B0C7K3N8ZR. Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it...
BALIGE Publishing, 2022. — 533 p. Welcome to “Machine Learning for Concrete Compressive Strength Analysis and Prediction with Python.” In this book, we will explore the fascinating field of applying machine learning techniques to analyze and predict the compressive strength of concrete. First, we will dive into the dataset, which includes various features related to concrete...
Apress Media LLC, 2020. — 300 p. — ISBN13: (electronic): 978-1-4842-5967-2. Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE. You’ll start...
Packt Publishing, 2020. — 490 p. — ISBN 9781800208919. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features Build, train, and deploy machine learning models quickly using Amazon SageMaker...
Independently published, 2019. — 128 р. — ISBN: 1092554556. A baby learns to crawl, walk and then run. we are in the crawling stage when it comes to appllying Machine Learning. Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that makes...
Manning Publ., 2023. — 168 p. Develop a mathematical intuition for how Machine Learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field...
Wiley-Scrivener Publishing, 2021. — 305 p. — ISBN 978-1-119-76885-2. Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine...
IGI Global, 2024. — 315 p. In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of...
O’Reilly Media, Inc., 2023. — 325 р. — ISBN-13: 978-1-098-14682-5. Take a data-first and use-case–driven approach with Low-Code AI to understand Machine Learning and Deep Learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn...
Springer, 2021. — 147 p. — (Terrorism, Security, and Computation). — ISBN 3030606139, 9783030606138. This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram’s behavior and reduce attacks...
The MIT Press, 2022. — 325 p. — ISBN 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard Machine Learning (ML) techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely...
Springer, 2023 p. — 353 p. — (Lecture Notes in Energy 44). — ISBN 9783031162480. Машинное обучение и его применение к реагирующим потокам: машинное обучение и сжигание This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of...
2nd Edition. — Apress, 2019. — 469 p. — ISBN13: (electronic): 978-1-4842-4947-5. Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two...
2nd Edition. — Apress, 2019. — 469 p. — ISBN13: (electronic): 978-1-4842-4947-5. Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two...
CRC Press, 2025. — 171 p. — (Innovations in Multimedia, Virtual Reality and Augmentation). — ISBN 978-1-032-76147-3. Машинное обучение в мультимедиа: раскрытие потенциала визуального и слухового интеллекта This book explores the interdisciplinary nature of Machine Learning in multimedia, highlighting its intersections with fields such as computer vision, natural language...
Manning Publications, 2024. — 375 р. — ISBN: 978-1617299025. Practical patterns for scaling Machine Learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Construct machine learning pipelines with data ingestion, distributed...
3rd edition. — Scatterplot Press, 2021. — 191 p. — ISBN B08RWBSKQB. Featured by Tableau as the first of “7 Books About Machine Learning for Beginners.” Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile? Well, hold on there… Before you embark on your journey, there are some high-level theory and...
LazyProgrammer, 2016. — 66 p. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Think about the large amounts of data being collected...
LazyProgrammer, 2016. — 66 p. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Think about the large amounts of data being collected...
Springer, 2024. — 365 p. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of Machine Learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind...
Leanpub, 2019. — 133 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
Reactive Publishing, December 28, 2023. — 371 p. Dive into the world of Artificial Intelligence with "Unsupervised Machine Learning with Python," the essential guide forprofessionals eager to master the most sophisticated analysis skills and unlock new dimensions of data interpretation. Building on the knowledge foundation of those who have already ventured into the realm of...
Reactive Publishing, 2024. — 497 p. — (Python Libraries for Finance Book 9). — ASIN: B0D66BR1B1. "Machine Learning: Scikit-Learn for Finance" bridges the gap between the complex world of machine learning and practical financial applications. With a focus on hands-on examples and real-world scenarios, this book is designed to equip readers with the skills to implement...
Morgan & Claypool, 2018. — 169 p. — ISBN: 9781681733968 (ebook). Synthesis Lectures on Artificial Intelligence and Machine Learning The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language,...
2nd Edition. — Springer, 2022. — 476 p. — ISBN 978-3-031-06649-8. This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the...
Independently published, 2019. — 129 p. — ISBN: 978-1075281518. Do you want to learn about machine learning? Are you feeling left out in the rat race where everyone is brushing up on their knowledge in the fields of AI and machine learning? Are you on a time crunch and don’t have enough time or resources to take a formal course on machine learning? If the answer to any of the...
John Wiley & Sons, 2023. — 823 p. — ISBN 9781119817505. There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning...
Elsevier, 2023. — 254 p. — ISBN 978-0-323-96126-4. Машинное обучение для транспортных исследований и приложений Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in Machine Learning provide new methods to tackle challenging...
Independently Published, 2019. — 98 p. — ASIN B082MPL2Z2. One of the most widely recognized AI methods utilized for handling huge information is AI, a self-versatile calculation that shows signs of improvement examination and examples with experience or with recently included information. In the event that a computerized installment organization needed to identify the event or...
CRC Press, 2020. — 204 p. — ISBN13: 978-1-138-32822-8. In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have...
CRC Press, 2020. — 204 p. — ISBN13: 978-1-138-32822-8. In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have...
Springer Singapore, 2023. — 133 p. — eBook ISBN: 978-981-99-7657-7. Presents state-of-the-art dirty data processing techniques for use in data pre-processing Opens promising avenues for the further study of dirty data processing Offers valuable take-away suggestions on dirty data processing for machine learning tasks In both the database and machine learning communities, data...
Springer, 2021. — 460 p. — ISBN 978-981-15-1966-6. An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and...
Springer, 2023. — 426 p. This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of Machine Learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its theoretical elaboration for...
М.: Наука, 1970. — 384 с. Особенность процедур метода потенциальных функций состоит в том, что при их применении к конкретным задачам приходится каждый раз выбирать вид некоторой функции, содержащейся в этой процедуре (такая функция называется «потенциальной»), либо выбирать систему функций, по которой могут раскладываться в ряды как потенциальная функция, так и выстраиваемые...
Алматы: Дарын, 2022. — 150 б. Машиналық оқытудың қосалқы тармағы болып саналатын терең оқыту - адамның араласуынсыз алгоритмдер мен үлкен деректер жиынын пайдаланып үлгілерді жасайтын.
М.: ДМК Пресс, 2015. — 400 c. — ISBN 9785970602737, 5970602736. Перед вами один из самых интересных учебников по машинному обучению – разделу искусственного интеллекта, изучающего методы построения моделей, способных обучаться, и алгоритмов для их построения и обучения. Автор воздал должное невероятному богатству предмета и не упустил из виду объединяющих принципов. Читатель с...
Springer, 2023. — 235 р. — ISBN 978-3-031-18552-6. The Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modelling, and Applications presents the state of the art of the application of Machine Learning (ML) and Deep Learning (DL) in the domain of finance. We will present a combination of empirical evidence to diverse fields of finance so...
Apress Media LLC., 2020. — 177 p. — ISBN13: (electronic): 978-1-4842-6579-6. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what...
Apress Media LLC., 2020. — 177 p. — ISBN13: (electronic): 978-1-4842-6579-6. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what...
IGI Global, 2023. — 385 p. — ISBN-13: 978-1668456446. In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address...
CRC Press, 2023. — 378 p. — ISBN 978-1-003-17025-9. Стратегии после усадки в статистическом и машинном обучении для данных высокой размерности This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in Data Science. It combines statistical learning and Machine Learning techniques in a unique and optimal way....
CRC Press, 2023. — 378 p. — ISBN 978-1-003-17025-9. Стратегии после усадки в статистическом и машинном обучении для данных высокой размерности This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in Data Science. It combines statistical learning and Machine Learning techniques in a unique and optimal way....
AI Publishing LLC, 2020. — 310 p. — ISBN B08LSLHBR8. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce,...
AI Publishing, 2020. — 308 p. — ISBN B08QJMNVCX. 10 Machine Learning Projects Explained from Scratch Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful...
Packt, 2022. — 408 p. — ISBN 1801811911, 9781801811910. Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key Features Learn the applications of machine learning in biotechnology and life science sectors Discover exciting real-world applications of deep learning and natural language...
Packt, 2020. — 384 p. — ISBN: 9781838826048. Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the art of data-driven problem-solving with hands-on...
Independently published, 2024. — 331 p. — ASIN : B0CW1C5S62. Many believe that a machine learning model, once trained, can act autonomously. This misconception has hindered innovation in ML/AI for far too long. In reality, ML models require integration within a comprehensive system encompassing inputs, processing, and outputs. My new book, "Applied Machine Learning: A Practical...
Apress Media LLC., 2020. — 262 p. — ISBN13: (electronic): 978-1-4842-5772-2. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine...
Apress Media LLC., 2024. — 478 р. — ISBN-13: 979-8-8688-0354-3. This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various...
BPB Publications, 2024. — 392 p. — ISBN 978-93-55519-818. Build high-impact ML/AI solutions by optimizing each step Key Features Build and fine-tune models for maximum performance. Practical tips to make your own state-of-the-art AI/ML models. ML/AI problem solving tips with multiple case studies to tackle real-world challenges. Description This book approaches data science...
BPB Publications, 2023. — 353 p. — ISBN 13: 978-9355518446. Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers. Key Features Gain a comprehensive understanding of TinyML's core concepts. Learn how to design your own TinyML applications from the ground up. Explore cutting-edge models, hardware, and software...
BPB Publications, 2023. — 353 p. — ISBN 13: 978-9355518446. Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers. Key Features Gain a comprehensive understanding of TinyML's core concepts. Learn how to design your own TinyML applications from the ground up. Explore cutting-edge models, hardware, and software...
Tarkeshwar Barua, Kamal Kant Hiran, Ritesh Kumar Jain, Ruchi Doshi. — Walter de Gruyter, 2024. — 487 p. — (De Gruyter STEM)/ — ISBN 13: 9783110697162. This book explains how to use the programming language Python to develop machine learning and deep learning tasks. It provides readers with a solid foundation in the fundamentals of machine learning algorithms and techniques. The...
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur. — 2nd Edition. — Packt Publishing, 2020. — 488 p. — ISBN: 978-1-80020-904-6. Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing....
2nd Edition. — BPB Publications, 2024. — 472 р. — ASIN: B0CLL37MFL. The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in Machine Learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection,...
BPB Publications, 2020 — 262 p. — ISBN 978-93-89845-42-6. Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key Features ● Understand the types of Machine learning. ● Get familiar with different Feature extraction methods. ● Get an overview of how Neural Network Algorithms work. ● Learn how to implement Decision Trees and Random Forests. ●...
BPB Publications, 2020 — 262 p. — ISBN 978-93-89845-42-6. Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key Features ● Understand the types of Machine learning. ● Get familiar with different Feature extraction methods. ● Get an overview of how Neural Network Algorithms work. ● Learn how to implement Decision Trees and Random Forests. ●...
Packt Publishing, 2019. — 158 p. — ISBN: 978-1-83855-035-6. Code files only! This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Machine learning is an essential...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Packt Publishing, 2020. — 740 p. — ISBN: 978-1838820299 2nd.ed. Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time...
Packt Publishing, 2018. — 646 p. — ISBN: 978-1-78862-111-3. Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models Explore and master the most important algorithms for solving complex machine learning problems. Machine learning is a subset of AI that aims to make modern-day computer systems smarter and...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 418 p. — ISBN: 978‐1‐119‐56195‐8 (ebk). This book, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and...
Globaltech NTC, 2018. — 144 р. You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been...
Independently published, 2020. — 294 p. — ISBN B085PSKBRH. 3 Books in 1! Are you searching for the fastest way to discover the secrets of the fascinating world of Computer Science? For a very limited time, you have the opportunity to get three best-selling guides in a single phenomenal mega bundle: if you are a student or a professional looking for more technical skills, then...
Independently published, 2020. — 294 p. — ISBN B085PSKBRH. 3 Books in 1! Are you searching for the fastest way to discover the secrets of the fascinating world of Computer Science? For a very limited time, you have the opportunity to get three best-selling guides in a single phenomenal mega bundle: if you are a student or a professional looking for more technical skills, then...
Springer, 2023. — 295 p. — (Applied Mathematical Sciences). The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider's view of how to combine two mature fields, scientific computing and...
Independently published, 2022. — 98 p. — ASIN: B09QYQC31S. Have you thought about a career in data science? It’s where the money is right now, and it’s only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it’s the next logical step. Machine learning is a subsection...
Independently published, 2022. — 98 p. — ASIN: B09QYQC31S. Have you thought about a career in data science? It’s where the money is right now, and it’s only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it’s the next logical step. Machine learning is a subsection...
Mercury Learning & Information, 2020. — 261 p. — ISBN: 1683924703 As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several...
Mercury Learning and Information, 2024. — 169 p. — ISBN-13: 978-150152-248-2. This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an...
Mercury Learning & Information, 2020. — 363 p. — ISBN: 978-1-68392-495-1. This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is...
Amazon.com Services LLC., 2020. — 102 p. Are you looking for a guide that will teach you all you need to know about machine learning? Are you looking for a way to learn how to write algorithms from scratch? Then read on… Artificial intelligence is a common part of our lives, and we use it daily. Machine learning is one application of artificial intelligence and is where...
Amazon.com Services LLC., 2020. — 102 p. Are you looking for a guide that will teach you all you need to know about machine learning? Are you looking for a way to learn how to write algorithms from scratch? Then read on… Artificial intelligence is a common part of our lives, and we use it daily. Machine learning is one application of artificial intelligence and is where...
Packt, 2020. — 256 p. — ISBN: 9781789801781. Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular machine learning...
Packt, 2020. — 256 p. — ISBN: 9781789801781. Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular machine learning...
Apress, 2019. — 248 p. — ISBN13: (electronic): 978-1-4842-5107-2. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in...
Packt, 2021 — 344 p. — ISBN 1801076804, 9781801076807. Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key Features Get well-versed with DataRobot features using real-world examples Use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle Make use of...
Chapman & Hall, 2025. — 344 p. — eBook ISBN 978-1-003-29633-1. This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the...
Independently published, 2021. — 225 p. — ASIN B09BNYDH5F. Have you always been curious about machine learning but do not know where to start. Or perhaps your new job requires you to learn machine learning but you are overwhelmed with all the information online. What is machine learning? What is Scikit-Learn? What does the fit() method that you see on so many online tutorials...
Manning Publications, 2023. — 335 p. — ISBN: 978-1617298042. Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your Machine Learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation...
O’Reilly Media, Inc., 2024. — 307 р. — ISBN-13: 978-1-098-14654-2. As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader...
O’Reilly Media, Inc., 2024. — 307 р. — ISBN-13: 978-1-098-14654-2. As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader...
Bentham Science Publishers, 2022. — 360 p. — ISBN 978-1-68108-940-9. Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include...
Wiley, STE Ltd., 2022. — 255 p. — (Computer Science, Operational Research and Decision). — ISBN 978-1-78945-071-2. Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the...
Wiley, STE Ltd., 2022. — 255 p. — (Computer Science, Operational Research and Decision). — ISBN 978-1-78945-071-2. Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the...
Springer Cham, 2024. — 119 p. — (Intelligent Systems Reference Library, volume 255). — eBook ISBN 978-3-031-59811-1. Presents the novel logo detection methods using machine learning paradigms Demonstrates the merits of the presented approaches over the reported approaches using the real-world applications Includes the state-of-the-art machine learning paradigms This book...
2nd Edition. — Packt Publishing, 2019. — 372 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
2nd Edition. — Packt Publishing, 2019. — 372 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
Independently published, 2020. — 201 p. — ISBN: 979-8611356791. Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? Or perhaps you want to know how to analyze data to solve simple domestic issues but you don’t know how to do it? Here’s the deal… As a beginner you will be probably afraid that...
Independently published, 2020. — 210 p. — ISBN: 979-8611346952. Would you like to learn how to use Python to generate machine learning models but you think it would be too difficult? Or perhaps you want to automate simple things with your computer but you don’t know how to do it? Here’s the deal… As a beginner you might think that programming is complex… Learning artificial...
2nd Edition. — O’Reilly Media, Inc., 2024. — 556 p. — ISBN 978-1-098-15601-5. Производственные системы машинного обучения: инженерные модели и конвейеры машинного обучения. Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from...
CRC Press, 2024. — 289 p. — ISBN: 978-1-003-35010-1. Machine Learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards Computer Science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of Machine Learning and look ahead to applications and...
CRC Press, 2024. — 289 p. — ISBN: 978-1-003-35010-1. Machine Learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards Computer Science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of Machine Learning and look ahead to applications and...
Packt Publishing, 2019. — 324 p. — ISBN: 978-1-83864-535-9. This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. AI is changing the world – and with this book, anyone can start building intelligent software! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to...
BPB Publications, 2023. — 150 р. — ISBN-13: 978-93-55511-133. A guide to understand the basics of Machine Learning and its application in the field of education. Key Features: - Create a more efficient and effective learning environment that meets the needs of all students. - Learn how to use the profound Machine learning advancements in the field of education. - Understand how...
BPB Publications, 2023. — 150 р. — ISBN-13: 978-93-55511-133. A guide to understand the basics of Machine Learning and its application in the field of education. Key Features: - Create a more efficient and effective learning environment that meets the needs of all students. - Learn how to use the profound Machine learning advancements in the field of education. - Understand how...
Springer Singapore, 2023. — 295 p. — (Algorithms for Intelligent Systems) — eBook ISBN: 978-981-99-6645-5. Provides rich set of chapters of machine learning and metaheuristic optimization Emphasizes optimization algorithms such as PSO, ant colony optimization, cuckoo search algorithm, etc. Includes real-world examples with attention to theoretical aspects for better...
Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad (Editor). — Wiley, 2020. — 535 p. — ISBN: 978-1119654742. Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online...
Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad (Editor). — Wiley, 2020. — 535 p. — ISBN: 978-1119654742. Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online...
Leanpub, 2023. — 192 р. This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on...
Leanpub, 2023. — 192 р. This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on...
Manning Publications, 2020. — 220 p. — ISBN: 978-1617294884. How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore...
Manning Publications, 2020. — 220 p. — ISBN: 978-1617294884. How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore...
Amazon.com Services LLC, 2020. — 120 p. — ISBN: 979-8615125096. You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you’ve always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and...
Leanpub, 2022-08-24. — 52 р. Learn how to implement various feature selection methods in a few lines of code utilizing the open-source Python library Feature-engine. Feature-engine is an open-source Python library for feature engineering and feature selection. It uses Pandas and Scikit-learn under the hood to engineer and select feature subsets. Feature selection is the process...
Leanpub, 2022-08-24. — 52 р. Learn how to implement various feature selection methods in a few lines of code utilizing the open-source Python library Feature-engine. Feature-engine is an open-source Python library for feature engineering and feature selection. It uses Pandas and Scikit-learn under the hood to engineer and select feature subsets. Feature selection is the process...
Leanpub, 2022-08-22. — 155 р. Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable Machine Learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods. Feature...
Leanpub, 2022-08-22. — 155 р. Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable Machine Learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods. Feature...
BPB Publications, 2021. — 218 p. — ISBN: 978-93-90684-700. Utilize Python and IBM Watson to put real-life use cases into production. Key Features Use of popular Python packages for building Machine Learning solutions from scratch. Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. Expert-led best practices to put...
KJ Publishing, 2022. — 166 p. Machine Learning is a complex subject area. Our goal in this lesson is to introduce you to some of the most common terms and ideas used in Machine Learning. I will then walk you through the different steps involved in Machine Learning (ML) and finish with a series of examples that use Machine Learning to solve real-world situations. Machine...
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
BPB Publications, 2025. — 346 р. — ISBN: 978-93-65891-997. Description This book prepares you with the knowledge and skills to confidently excel in the exciting world of machine learning (ML) interviews and launch a successful career in this dynamic field. This book offers a collection of curated questions and answers to help readers understand key ML concepts, including data...
CRC Press, 2019. — 364 p. — ISBN 13 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
Springer, 2019. — 372 p. — ISBN: 978-3-030-21810-2 (eBook). This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the...
Independently published, 2024. — 107 р. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
Independently published, 2024. — 107 р. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
Orchid Publishing, 2023. — 431 p. Are you ready to dive into the fascinating world of Machine Learning and Artificial Intelligence? Do you want to understand the technology that powers everything from personalized recommendations to self-driving cars? If so, "Machine Learning With Python Programming : 2023 A Beginners Guide" is the book you've been waiting for. This...
Springer/Posts & Telecom Press, 2023. — 398 p. Firstly, it takes common competition scenarios as a guide by giving the main processes of using Machine Learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover,...
Apress, 2021. — 300 p. — ISBN-13 978-1-4842-7031-8. Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book...
CRC Press, 2023. — 336 p. — ISBN: 978-1003187158. Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and...
CRC Press, 2023. — 336 p. — ISBN: 978-1003187158. Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and...
Springer, 2023. — 319 p. — (Artificial Intelligence: Foundations, Theory, and Algorithms). Machine Learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it...
Sonar Publishing, 2023. — 222 p. — ISBN-13: 979-8867285340. "Python for Machine Learning: From Fundamentals to Real-World Applications" is your comprehensive roadmap to mastering Machine Learning with Python. Whether you're a beginner looking to enter the exciting world of Data Science or an experienced programmer aiming to delve deeper into Machine Learning, this book provides...
Springer, 2021. - 411p. - ISBN 9783030658991 This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine...
HiTeX Press;, 2024. — 403 p. — ASIN: B0DL4V4L2G. "Few-Shot Machine Learning: Doing More with Less Data" is an illuminating exploration into the cutting-edge techniques that enable machines to learn efficiently from limited data. This book delves deep into the domain of few-shot learning—a revolutionary approach that challenges the traditional dependency on vast datasets. By...
Packt, 2018. — 172 p. — ISBN: 1789343704. Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
CRC Press, 2023. — 298 p. — ISBN 978-1003002611. Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and...
Packt Publishing, 2018. — 367 p. — ISBN: 978-1-78899-824-6. A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added...
Packt Publishing, 2017. — 420 p. — ISBN: 978-1-78728-074-8. Key Features Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Book Description Join...
O’Reilly Media, Inc. 2023. — 250 p. — ISBN 978-1-492-09767-9. Whether based on academic theories or discovered empirically by humans and machines, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability...
Independently published, 2021. — 674 p. — ISBN B08V4VZSCH. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine...
2nd Edition. — Apress Berkeley, 2023. — 661 p. — eBook ISBN: 978-1-4842-9801-5. This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry...
2nd Edition. — Apress Media, LLC,, 2024. — 676 p. — ISBN: 978-1-4842-9801-5. This new and updated edition takes you through the details of Machine Learning to give you an understanding of cognitive computing, IoT, Big Data, AI, quantum computing, and more. The book explains how Machine Learning techniques are used to solve fundamental and complex societal and industry...
Manning Publications, 2024. — 504 p. — ISBN-13: 978-1633438835. Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization...
O’Reilly Media, 2017. — 99 р. — ISBN: 978-1-491-92406-8. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
O’Reilly Media, 2017. — 99 р. — ISBN: 978-1-491-92406-8. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
Apress, 2020. — 146 p. — ISBN13: (electronic): 978-1-4842-5940-5. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain...
Springer, 2021. — 349 p. — ISBN 9789811594915. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these...
Packt Publishing, 2019. — 284 p. — ISBN: 978-1-78961-285-1. Code files only! Combine popular machine learning techniques to create ensemble models using Python Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak...
No Starch Press, 2021. — 291 p. — ISBN 978-1-7185-0056-3. A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial...
Manning Publications, 2023. — 362 p. — (Final Release). — ISBN 978-1617299520. Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Summary In Evolutionary Deep Learning you will...
O’Reilly Media, Inc., 2023. — 290 p. — ISBN-13: 978-1-098-10665-2. With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph,...
O’Reilly Media, Inc., 2023. — 290 p. — ISBN-13: 978-1-098-10665-2. With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph,...
John Wiley & Sons, Inc., 2019. — 307 p. — ISBN: 978-1-119-54567-5. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on...
Addison-Wesley Professional, 2025. — 224 p. — (Addison-Wesley Data & Analytics Series). — ISBN-13: 978-0-13-834074-2. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris...
Addison-Wesley Professional, 2025. — 224 p. — (Addison-Wesley Data & Analytics Series). — ISBN-13: 978-0-13-834074-2. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris...
BPB Publications, 2023. — 250 p. — ISBN 978-93-5551-784-5. Learn how to use AutoML to leverage Machine Learning for solving business problems. Key Features - Get familiar with the common machine learning problems and understand how to solve them. - Understand the importance of different types of data and how to work with them effectively. - Learn how to use machine learning and...
3rd edition. — Packt Publishing, 2020. — 526 p. — ISBN: 1800209711, 978-1800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data...
Bentham Books, 2022. — 240 р. — ISBN 978-981-5079-19-7. This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it...
Bentham Books, 2022. — 240 р. — ISBN 978-981-5079-19-7. This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it...
Wiley-IEEE, 2020. — 496 p. — ISBN 978-1119562313(EPUB). A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the...
Orange Education Pvt. Ltd , 2024. — 247 р. — ISBN 8197256373, 978-8197256370, ASIN B0D8L3Q283. Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it...
Orange Education Pvt. Ltd , 2024. — 247 р. — ISBN 8197256373, 978-8197256370, ASIN B0D8L3Q283. Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it...
Dr. Wasim Akram Mandal. — 2025. — 76 p. Artificial intelligence is evolving at an unprecedented pace, and new breakthroughs continue to reshape the way we interact with technology. While OpenAI's ChatGPT has dominated the AI space, a new contender has emerged—DeepSeek AI, an innovative and powerful language model that challenges the status quo. This book, Mastering DeepSeek AI:...
River Publishers, 2025. — 172 р. — ISBN 978-87-7004-654-1. This is an essential resource for beginners and experienced practitioners in Machine Learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow,...
River Publishers, 2025. — 172 р. — ISBN 978-87-7004-654-1. This is an essential resource for beginners and experienced practitioners in Machine Learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow,...
Packt Publishing, 2021. — 344 p. — ISBN 978-1-80107-812-2. Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key Features Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML Leverage SQL syntax to train, evaluate,...
Independently published, 2023. — 245 p. In the ever-changing world of finance and trading, the search for a competitive edge has been a constant driver of innovation. Over the last few decades, the field of quantitative trading has emerged as a powerful force, pushing the boundaries of what is possible and reshaping the way we approach the market. At the heart of this...
Independently published, 2023. — 245 p. In the ever-changing world of finance and trading, the search for a competitive edge has been a constant driver of innovation. Over the last few decades, the field of quantitative trading has emerged as a powerful force, pushing the boundaries of what is possible and reshaping the way we approach the market. At the heart of this...
Academic Press/Elsevier, 2023. — 222 p. Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The...
Academic Press/Elsevier, 2023. — 222 р. — ISBN: 978-0-443-19035-3. Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous...
No Starch Press, 2024. — 272 p. — ISBN-13: 978-1718502109. Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-52-7. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-64-0. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-52-7. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence...
Wiley & Sons, Inc., 2020. — 336 p. — ISBN: 978-1-119-60290-3 (ebk). Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence...
Wiley & Sons, Inc., 2020. — 336 p. — ISBN: 978-1-119-60290-3 (ebk). Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence...
Independently published, 2021. — 93 p. — ASIN B0952D2T8C. The Machine Learning process is revolutionizing many sectors in engineering including manufacturing sectors such as the Friction Stir Welding Process. The implementation of machine learning algorithms in Friction Stir Welding process not only reduced the time of the experiment but also reduces the cost of the experiment....
Springer, 2018. — 93 p. — ISBN10: 331975713X; ISBN13: 978-3319757131. This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior. The author describes techniques to reduce significantly the time required for simulations involving...
Scrivener Publishing, 2021. — 528 p. — ISBN 978-1-119-78580-4. In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise...
2nd Edition. — The MIT Press, 2019. — 504 р. — (Adaptive Computation and Machine Learning series). — ISBN: 978-0262039406. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It...
2nd Edition. — The MIT Press, 2019. — 504 р. — (Adaptive Computation and Machine Learning series). — ISBN: 978-0262039406. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It...
2nd Edition. — Leanpub, 2022-03-04. — 329 р. This book teaches you how to make Machine Learning models more interpretable. Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of Machine Learning. This book is about making Machine Learning models and their...
Leanpub, 2023-02-14. — 101 р. This book teaches you how to quantify the uncertainty of machine learning models with conformal prediction in Python. Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification. Summary A prerequisite for trust in Machine Learning is uncertainty...
Manning Publications, 2021. — 426 p. — ISBN 9781617296741. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction...
De Gruyter, 2023. — 506 р. — ISBN: 978-3-11-078612-5. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 506 р. — ISBN: 978-3-11-078612-5. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 478 р. — ISBN 978-3-11-078614-9. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 364 р. — ISBN 978-3-11-078613-2. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 364 р. — ISBN 978-3-11-078613-2. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
Leanpub, 2022. — 112 p. Build a career while doing technical wriitng. I earned $300 for my first paid Data Science and Machine Learning article. I get paid between $250 and $500 for each Data Science article I write. In this ebook, I'll show you how you too, can earn while writing about Data Science and Machine Learning. You have been learning about Data Science and Machine...
Apress, 2022. — 343 p. — ISBN 9781484278017, 9781484278024. Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms. You’ll begin by reviewing the theoretical aspects of machine learning...
Manning Publications, 2020. — 360 p. — ISBN: 978-1617295645. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core...
Apress Media LLC., 2020. — 570 p. — ISBN13: (electronic): 978-1-4842-5174-4. Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most...
Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango. — Chapman and Hall/CRC, 2025. — 192 р. — ISBN 978-87-7004-714-2. The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct...
Manning Publications, 2022. — 358 р. — ISBN: 978-1617299797. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This...
Apress Media LLC, 2022 — 347 p. — ISBN-13: 978-1-4842-7921-2. Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep...
Apress Media LLC, 2022 — 347 p. — ISBN-13: 978-1-4842-7921-2. Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep...
Apress Media LLC, 2022 — 347 p. — ISBN-13: 978-1-4842-7921-2. Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep...
3rd Edition. — Apress Media LLC., 2024. — 463 р. — ISBN 978-1-4842-9846-6. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem...
3rd Edition. — Apress Media LLC., 2024. — 463 р. — ISBN 978-1-4842-9846-6. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem...
BPB Publictions, 2019 — 280 p. — ISBN: 978-93-88511-13-1. Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite...
BPB Publictions, 2019 — 280 p. — ISBN: 978-93-88511-13-1. Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite...
Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli. — Apress Media LLC., 2020. — 320 p. — ISBN13: (electronic): 978-1-4842-6094-4. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow...
Independently published, 2021. — 201 p. — ISBN B08QGZJDHK. Master the world of machine learning and data science with this comprehensive beginner’s bundle. Data Science and Machine Learning are the biggest buzzwords in the business world nowadays. If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily – we have the...
Independently published, 2021. — 201 p. — ISBN B08QGZJDHK. Master the world of machine learning and data science with this comprehensive beginner’s bundle. Data Science and Machine Learning are the biggest buzzwords in the business world nowadays. If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily – we have the...
O’Reilly Media, 2021-12-10. — 362 p. — ISBN-13: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world’s data is unlabeled, conventional supervised learning cannot be applied;...
O’Reilly Media, 2020. — 358 p. — ISBN13: 978-3-96088-878-9. Ein Großteil der weltweit verfügbaren Daten ist ungelabelt. Auf diese nicht klassifizierten Daten lassen sich die Techniken des Supervised Learning, die im Machine Learning viel genutzt werden, nicht anwenden. Dagegen kann das Unsupervised Learning - auch unüberwachtes Lernen genannt - für ungelabelte Datensätze...
Packt Publishing, 2019. — 342 p. — ISBN: 978-1-78899-692-1. This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning...
Springer, 2021. — 197 p. — ISBN 978-3-030-72068-1. This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical...
Springer, 2022. — 630 p. — (Communications and Control Engineering). — ISBN 978-3-030-95860-2. This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power...
Packt Publishing, 2024. — 603 p. — ISBN 978-1-80512-250-0. The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI, 2nd Edition / Справочник архитектора решений для машинного обучения: Практические стратегии и рекомендации по жизненному циклу ML, системному проектированию, MLOps и...
O’Reilly Media, 2022. — 300 p. — ISBN-13: 978-1-098-10682-9. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you’re looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you....
CRC Press, 2024. — 226 p. — ISBN 978-1-032-47841-8, 978-1-032-48399-3, 978-1-003-38884-5. This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and...
O’Reilly Media, 2022. — 425 p. — ISBN 978-1-492-09805-8. While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest,...
O’Reilly Media, 2022. — 425 p. — ISBN 978-1-492-09805-8. While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest,...
O’Reilly Media, Inc., 2023. — 376 р. — ISBN 978-1-098-12027-6. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce...
O’Reilly Media, Inc., 2023. — 376 р. — ISBN 978-1-098-12027-6. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce...
O’Reilly Media, 2023. — 271 p. — ISBN-13: 978-1-098-11722-1 Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use...
Apress, 2021, - 127p. - ISBN 978-1912807130 Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an...
Apress, 2021, - 127p. - ISBN 978-1912807130 Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an...
Apress, 2020. — 315 p. — ISBN13: (electronic): 978-1-4842-5669-5. Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every...
Apress, 2020. — 315 p. — ISBN13: (electronic): 978-1-4842-5669-5. Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every...
Springer, 2023. — 312 p. — (Industrial and Applied Mathematics). — ISBN 978-981-19-6552-4. This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the...
Packt Publishing, 2021. — 270 p. — ISBN 978-1800567887. Code Files Only! Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network....
No Starch Press, 2024. — 264 р. — ISBN-13: 978-1-7185-0377-9. Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and...
No Starch Press, 2024. — 264 р. — ISBN-13: 978-1-7185-0377-9. Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Final version! Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of...
Manning Publications, 2020. — 528 p. — ISBN: 978-1617296574. Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools...
Manning Publications, 2020. — 528 p. — ISBN: 978-1617296574. Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools...
Lioncrest Publishing, 2021. — 222 p. — ISBN 978-1-5445-1882-4. How can you successfully deploy AI? When AI works, it’s nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren’t...
New York: amazon.com Services LLC, 2021. — 412 p. — ISBN B08SCQ2Y75. Machine learning can be broadly classified into supervised and unsupervised learning. By definition, the term supervised means that the “machine” (the system) learns with the help of something—typically a labeled training data. Training data (or a dataset ) is the basis on which the system learns to infer. An...
2nd Edition. — World Scientific Publishing Co. Pte. Ltd., 2019. — 302 p. — ISBN 978-981-120-195-0. Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition) (Series in Machine Perception and Artificial Intelligence) 2nd Edition This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories,...
CRC Press, 2021. — 221 p. — ISBN 9780367703868. Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This...
Packt Publishing, 2019. — 252 p.— ISBN 1838828974, 9781838828974. Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step...
Springer, 2022. — 128 p. This book discusses the Versatile Video Coding (VVC), the ISO and ITU state-of-the-art video coding standard. VVC reaches a compression efficiency significantly higher than its predecessor standard (HEVC) and it has a high versatility for efficient use in a broad range of applications and different types of video content, including Ultra-High Definition...
Packt, 2020. — 351 p. — ISBN: 9781839219061. Take a comprehensive and step-by-step approach to understanding machine learning Key Features Discover how to apply the scikit-learn uniform API in all types of machine learning models Understand the difference between supervised and unsupervised learning models Reinforce your understanding of machine learning concepts by working on...
Springer Singapore, 2024. — 127 p. — (Studies in Computational Intelligence, volume 1155). — eBook ISBN 978-981-97-2720-9. Covers three primary data types: numerical, textual, and image data Offers GitHub source code encompassing fundamental components and advanced machine learning tools Serves as a reference for researchers, students, practitioners, and policymakers Employing...
Packt Publishing, 2019. — 327 р. — ISBN 978-1-78913-660-9. Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble...
O’Reilly Media, Inc., 2024. — 329 p. — ISBN: 978-1-492-09452-4. Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In...
O’Reilly Media, Inc., 2024. — 329 p. — ISBN: 978-1-492-09452-4. Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In...
Packt, 2019. - 664p. - ISBN: 9781838827878 Your one-stop guide to working with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js A handy guide to make your web applications smart from scratch using example-based approach Use flexible and...
Independently published, 2022. — 240 p. — ISBN-10: B09HKHPT9M. Why was this book written? Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, AI Engineering being associated with machine learning. Our aim in this book is to demystify these concepts and provide...
Independently published, 2022. — 240 p. — ISBN-10: B09HKHPT9M. Why was this book written? Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, AI Engineering being associated with machine learning. Our aim in this book is to demystify these concepts and provide...
2nd Edition. — Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce. — Wiley, 2023. — 611 p. — ISBN 978-1119903833. MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,...
Independently published, 2021. — 269 p. — ISBN B089GQM3QD. Machine learning is a computer programming technique in which software is built in such a way that it can learn new facts from itself and make decisions on its own when necessary. Machine learning (ML) is a large discipline, and this book covers a lot of ground. We attempted to cover all aspects of the subject. This...
Independently published, 2021. — 269 p. — ISBN B089GQM3QD. Machine learning is a computer programming technique in which software is built in such a way that it can learn new facts from itself and make decisions on its own when necessary. Machine learning (ML) is a large discipline, and this book covers a lot of ground. We attempted to cover all aspects of the subject. This...
Independently published, 2021. — 269 p. — ISBN B089GQM3QD. Machine learning is a computer programming technique in which software is built in such a way that it can learn new facts from itself and make decisions on its own when necessary. Machine learning (ML) is a large discipline, and this book covers a lot of ground. We attempted to cover all aspects of the subject. This...
BALIGE Publishing, 2022. — 533 p. Welcome to “Machine Learning for Concrete Compressive Strength Analysis and Prediction with Python.” In this book, we will explore the fascinating field of applying machine learning techniques to analyze and predict the compressive strength of concrete. First, we will dive into the dataset, which includes various features related to concrete...
Morgan Kaufmann, 2019. — 327 p. — ISBN: 978-0-12-814623-1. Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional...
O’Reilly Media, 2023. — 450 р. — ISBN 978-1-098-12020-7. Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can...
O’Reilly Media, 2023. — 450 р. — ISBN 978-1-098-12020-7. Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can...
Packt Publishing, 2016. — 439 p. — ISBN: 978-1-7858-8721-5. Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage...
Manning Publications, 2018. — 256 p. — ISBN: 978-1-6172-9333-7. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
Packt, 2019. - 162p. - ISBN: 9781838825669 Teach your machine to think for itself! Key Features Delve into supervised learning and grasp how a machine learns from data Implement popular machine learning algorithms from scratch, developing a deep understanding along the way Explore some of the most popular scientific and mathematical libraries in the Python language Book...
Packt Publishing, 2021. — 194 p. — ISBN 978-1800567641. Code Files Only! Create better and easy-to-use deep learning models with AutoKeras Key Features Design and implement your own custom machine learning models using the features of AutoKeras Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis Get familiar with advanced...
O’Reilly Media, Inc., 2023. — 325 р. — ISBN-13: 978-1-098-14682-5. Take a data-first and use-case–driven approach with Low-Code AI to understand Machine Learning and Deep Learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn...
Springer, 2020. — 292 p. — ISBN: 978-3-030-37830-1 (eBook). This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network...
Springer, 2020. — 292 p. — ISBN: 978-3-030-37830-1 (eBook). This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network...
The MIT Press, 2022. — 325 p. — ISBN 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard Machine Learning (ML) techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely...
Apress, 2021. — 193 p. — ISBN 978-1484268421. Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable...
Apress, 2021. — 193 p. — ISBN 9781484268421. Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable...
Apress, 2021. — 193 p. — ISBN 9781484268421. Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable...
Springer, 2021. — 260 p. — ISBN 978-981-15-7876-2. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a...
O’Reilly Media, Inc., 2024. — 300 p. — ISBN: 978-1-098-14463-0. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung,...
O’Reilly Media, Inc., 2024. — 300 p. — ISBN: 978-1-098-14463-0. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung,...
Manning Publications, 2024. — 375 р. — ISBN: 978-1617299025. Practical patterns for scaling Machine Learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Construct machine learning pipelines with data ingestion, distributed...
Independently published, 2023. — 83 p. Machine Learning is a fascinating field that has the potential to revolutionize the way we live and work. However, it can be overwhelming for beginners to understand the complex algorithms and concepts involved. This book is designed to make Machine Learning accessible and easy to understand for anyone who wants to learn. The algorithms...
W. W. Norton & Company, 2024. — 176 p. — (A Norton Short). In the industrial age, automation came for the shoemaker and the seamstress. Today, it has come for the writer, physician, programmer, and attorney. Literary Theory for Robots reveals the hidden history of modern machine intelligence, taking readers on a spellbinding journey from medieval Arabic philosophy to visions of...
O’Reilly Media, Inc., 2024. — 359 p. — ISBN 978-1-098-15161-4. What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that...
O’Reilly Media, Inc., 2024. — 359 p. — ISBN 978-1-098-15161-4. What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that...
Oxford University Press, 2020. — 272 p. — ISBN: 978-0-19-256309-5. Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and...
Oxford University Press, 2020. — 272 p. — ISBN: 978-0-19-256309-5. Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and...
Packt Publishing, 2019. — 533 p. — ISBN: 978-1-78961-467-1. Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the...
CRC Press, 2025. — 264 p. — ISBN: 978-1-003-42590-8. This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences....
Wiley-IEEE Press, 2025. — 384 p. — ISBN-13: 978-1394294954. A practical guide to AI applications for Simple Python and MatLAB scripts Machine Learning and AI with Simple Python and MatLAB Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular...
Wiley-IEEE Press, 2025. — 384 p. — ISBN-13: 978-1394294954. A practical guide to AI applications for Simple Python and MatLAB scripts Machine Learning and AI with Simple Python and MatLAB Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular...
Reactive Publishing, December 28, 2023. — 371 p. Dive into the world of Artificial Intelligence with "Unsupervised Machine Learning with Python," the essential guide forprofessionals eager to master the most sophisticated analysis skills and unlock new dimensions of data interpretation. Building on the knowledge foundation of those who have already ventured into the realm of...
Independently Published, 2023-10-16. — 132 p. — ASIN: B0CL7M4LLZ. "Machine Learning Concepts from A to Z: A Comprehensive Guide with Code" Are you eager to unlock the potential of machine learning, from its fundamental principles to practical implementation? Look no further. "Machine Learning Concepts from A to Z" is your all-encompassing, go-to guide for understanding and...
Independently Published, 2023-10-16. — 132 p. — ASIN: B0CL7M4LLZ. "Machine Learning Concepts from A to Z: A Comprehensive Guide with Code" Are you eager to unlock the potential of machine learning, from its fundamental principles to practical implementation? Look no further. "Machine Learning Concepts from A to Z" is your all-encompassing, go-to guide for understanding and...
John Wiley & Sons Inc., 2024. — 512 p. — ISBN 978-1394220625. Теория и приложения машинного обучения: практические примеры использования Python на классических и квантовых машинах Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications...
John Wiley & Sons Inc., 2024. — 512 p. — ISBN 978-1394220625. Теория и приложения машинного обучения: практические примеры использования Python на классических и квантовых машинах Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications...
De Gruyter, 2024. — 210 р. — ISBN: 978-3-11-128981-6. This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory...
De Gruyter, 2024. — 210 р. — ISBN: 978-3-11-128981-6. This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory...
Apress Media LLC., 2020. — 392 p. — ISBN13: (electronic): 978-1-4842-6156-9. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as...
Apress Media LLC., 2020. — 392 p. — ISBN13: (electronic): 978-1-4842-6156-9. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as...
Packt, 2020. — 311 p. — ISBN: 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and tricks...
Packt Publishing, 2020. — 311 p. — ISBN 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and...
Packt Publishing, 2020. — 311 p. — ISBN 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and...
Johns Hopkins University Press, 2024. — 280 р. —ISBN: 978-1-4214-4923-4. How AI is revolutionizing the future of learning and how educators can adapt to this new era of human thinking. Artificial Intelligence (AI) is revolutionizing the way we learn, work, and think. Its integration into classrooms and workplaces is already underway, impacting and challenging ideas about...
Johns Hopkins University Press, 2024. — 280 р. —ISBN: 978-1-4214-4923-4. How AI is revolutionizing the future of learning and how educators can adapt to this new era of human thinking. Artificial Intelligence (AI) is revolutionizing the way we learn, work, and think. Its integration into classrooms and workplaces is already underway, impacting and challenging ideas about...
Independently Published, 2019. — 98 p. — ASIN B082MPL2Z2. One of the most widely recognized AI methods utilized for handling huge information is AI, a self-versatile calculation that shows signs of improvement examination and examples with experience or with recently included information. In the event that a computerized installment organization needed to identify the event or...
Independently Published, 2019. — 98 p. — ASIN B082MPL2Z2. One of the most widely recognized AI methods utilized for handling huge information is AI, a self-versatile calculation that shows signs of improvement examination and examples with experience or with recently included information. In the event that a computerized installment organization needed to identify the event or...
Independently published, 2020. — 335 p.— ISBN B08RCSJ39K. Mastering Deep Learning with Python: 2 Manuscripts This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and...
CRC Press, 2020. — 204 p. — ISBN13: 978-1-138-32822-8. In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have...
Academic Press/Elsevier, 2023. — 404 р. — ISBN 978-0-323-89931-4. Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve....
Academic Press/Elsevier, 2023. — 404 р. — ISBN 978-0-323-89931-4. Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve....
М.: ДМК Пресс, 2015. — 400 с.
Один из самых интересных учебников по машинному обучению - разделу искусственного интеллекта, изучающего методы построения моделей, способных обучаться, и алгоритмов для их построения и обучения. Автор воздал должное невероятному богатству предмета и не упустил из виду объединяющих принципов. Читатель с первых страниц видит машинное обучение в...
М.: ДМК Пресс, 2023. — 256 с.: ил. — ISBN 978-5-93700-196-2. Ошеломляющий успех коммерческих приложений машинного обучения (machine learning – ML) и быстрый рост этой отрасли создали высокий спрос на готовые методы ML, которые можно легко использовать без специальных знаний. Однако и сегодня успех практического применения в решающей степени зависит от экспертов – людей, которые...
IGI Global, 2024. —308 p. — eBook ISBN 9798369338841. Organizations worldwide grapple with the complexities of incorporating machine learning into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business...
Manning Publications, 2025. — 376 p. — ISBN-13: 978-1633438750. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside,...
Tarkeshwar Barua, Kamal Kant Hiran, Ritesh Kumar Jain, Ruchi Doshi. — Walter de Gruyter, 2024. — 487 p. — (De Gruyter STEM)/ — ISBN 13: 9783110697162. This book explains how to use the programming language Python to develop machine learning and deep learning tasks. It provides readers with a solid foundation in the fundamentals of machine learning algorithms and techniques. The...
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur. — 2nd Edition. — Packt Publishing, 2020. — 488 p. — ISBN: 978-1-80020-904-6. Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing....
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur. — 2nd Edition. — Packt Publishing, 2020. — 488 p. — ISBN: 978-1-80020-904-6. Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing....
Packt Publishing, 2019. — 211 p. — ISBN: 978-1-83855-035-6. This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Machine learning is an essential part of today’s...
Packt Publishing, 2019. — 211 p. — ISBN: 978-1-83855-035-6. This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Machine learning is an essential part of today’s...
Packt Publishing, 2019. — 426 p. — ISBN: 978-1-78934-827-9. Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster...
Packt Publishing, 2019. — 426 p. — ISBN: 978-1-78934-827-9. Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster...
2nd Edition. — Packt Publishing, 2018. — 604 p. — ISBN: 978-1-78934-799-9. Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Machine learning has gained tremendous popularity for its powerful and fast...
Packt, 2020. — 256 p. — ISBN: 9781789801781. Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular machine learning...
2nd Edition. — Packt Publishing, 2019. — 371 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
2nd Edition. — Packt Publishing, 2019. — 433 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
2nd Edition. — Packt Publishing, 2019. — 433 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
2nd Edition. — Packt Publishing, 2019. — 433 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
Wiley-IEEE Press, 2025. — 445 р. — ISBN: 978-1394272945. Enables readers to develop foundational and advanced vectorization skills for scalable Data Science and Machine Learning and address real-world problems. Offering insights across various domains such as Computer Vision and natural language processing (NLP), Vectorization covers the fundamental topics of vectorization...
Wiley-IEEE Press, 2025. — 445 р. — ISBN: 978-1394272945. Enables readers to develop foundational and advanced vectorization skills for scalable Data Science and Machine Learning and address real-world problems. Offering insights across various domains such as Computer Vision and natural language processing (NLP), Vectorization covers the fundamental topics of vectorization...
CRC Press, 2024. — 289 p. — ISBN: 978-1-003-35010-1. Machine Learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards Computer Science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of Machine Learning and look ahead to applications and...
Packt Publishing, 2019. — 324 p. — ISBN: 978-1-83864-535-9. This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. AI is changing the world – and with this book, anyone can start building intelligent software! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to...
Packt Publishing, 2020. — 477 p. — ISBN: 978-1-83882-441-9. Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book,...
Packt Publishing, 2020. — 477 p. — ISBN: 978-1-83882-441-9. Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book,...
Packt Publishing, 2020. — 477 p. — ISBN: 978-1-83882-441-9. Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book,...
BPB Publications, 2025. — 346 р. — ISBN: 978-93-65891-997. Description This book prepares you with the knowledge and skills to confidently excel in the exciting world of machine learning (ML) interviews and launch a successful career in this dynamic field. This book offers a collection of curated questions and answers to help readers understand key ML concepts, including data...
Packt Publishing, 2019. — 295 p. — ISBN: 978-1-78934-580-3. Leverage the power of reward-based training for your deep learning models with Python Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to...
Packt Publishing, 2019. — 295 p. — ISBN: 978-1-78934-580-3. Leverage the power of reward-based training for your deep learning models with Python Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to...
Packt Publishing, 2019. — 295 p. — ISBN: 978-1-78934-580-3. Leverage the power of reward-based training for your deep learning models with Python Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to...
Birmingham: Packt Publishing, 2018. — 305 p. — ISBN: 1788992288. Get into the world of smart data security using machine learning algorithms and Python libraries ! Key Features Learn machine learning algorithms and cybersecurity fundamentals. Automate your daily workflow by applying use cases to many facets of security. Implement smart machine learning solutions to detect...
Packt Publishing, 2019. — 563 p. — ISBN: 978-1-78934-634-3. Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and...
Packt Publishing, 2019. — 563 p. — ISBN: 978-1-78934-634-3. Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and...
2nd Revised Edition. — Cambridge University Press, 2025. — 426 p. — ISBN-13: 978-1-316-51886-1. As Machine Learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers...
2nd Revised Edition. — Cambridge University Press, 2025. — 426 p. — ISBN-13: 978-1-316-51886-1. As Machine Learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers...
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu. — Packt Publishing, 2019. — 428 p. —ISBN: 978-1-83855-013-4. Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems With huge amounts of data being generated every moment, businesses need applications that apply complex...
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu. — Packt Publishing, 2019. — 428 p. — ISBN: 978-1-83855-013-4. Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems With huge amounts of data being generated every moment, businesses need applications that apply complex...
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu. — Packt Publishing, 2019. — 428 p. —ISBN: 978-1-83855-013-4. Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems With huge amounts of data being generated every moment, businesses need applications that apply complex...
HiTeX Press;, 2024. — 403 p. — ASIN: B0DL4V4L2G. "Few-Shot Machine Learning: Doing More with Less Data" is an illuminating exploration into the cutting-edge techniques that enable machines to learn efficiently from limited data. This book delves deep into the domain of few-shot learning—a revolutionary approach that challenges the traditional dependency on vast datasets. By...
Packt Publishing, 2019. — 186 p. — ISBN: 978-1-78995-229-2. Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using...
Packt Publishing, 2019. — 186 p. — ISBN: 978-1-78995-229-2. Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using...
Packt Publishing, 2019. — 380 p. — ISBN: 978-1-78995-492-0. Explore the exciting world of machine learning with the fastest growing technology in the world Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply...
Packt Publishing, 2019. — 380 p. — ISBN: 978-1-78995-492-0. Explore the exciting world of machine learning with the fastest growing technology in the world Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply...
Packt, 2018. — 172 p. — ISBN: 1789343704. Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight from volumes of...
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight from volumes of...
2nd Edition. — Packt Publishing, 2019. — 326 p. — ISBN: 978-1-78899-459-0. Bring magic to your mobile apps using TensorFlow Lite and Core ML Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with...
2nd Edition. — Packt Publishing, 2019. — 326 p. — ISBN: 978-1-78899-459-0. Bring magic to your mobile apps using TensorFlow Lite and Core ML Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with...
Manning Publications, 2024. — 504 p. — ISBN-13: 978-1633438835. Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization...
Packt Publishing Ltd., 2020. — 530 p. — ISBN: 978-1-78995-533-0. Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn...
Packt Publishing Ltd., 2020. — 530 p. — ISBN: 978-1-78995-533-0. Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn...
Packt Publishing, 2019. — 386 p. — ISBN: 978-1-78961-285-1. Combine popular machine learning techniques to create ensemble models using Python Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to...
Packt Publishing, 2019. — 386 p. — ISBN: 978-1-78961-285-1. Combine popular machine learning techniques to create ensemble models using Python Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to...
Packt Publishing, 2021. — 249 p. — ISBN 978-1-80056-079-6. Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow Use MLflow to iteratively develop a ML model and manage it Discover and work with the...
3rd edition. — Packt Publishing, 2020. — 527 p. — ISBN 9781800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data scientists...
River Publishers, 2025. — 172 р. — ISBN 978-87-7004-654-1. This is an essential resource for beginners and experienced practitioners in Machine Learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow,...
Packt Publishing, 2021. — 737 p. — ASIN B08PDFXXRL. Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with...
Packt Publishing, 2019. — 408 p. — ISBN: 978-1-78934-979-5. Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing...
Packt Publishing, 2019. — 408 p. — ISBN: 978-1-78934-979-5. Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing...
Packt Publishing, 2019. — 408 p. — ISBN: 978-1-78934-979-5. Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing...
John Wiley & Sons, 2021. — 464 p. — (For Dummies). 2nd Edition Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android?as in the movie Ex Machina?it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving...
Packt Publishing, 2022. - 376 p. - ISBN 180324710X. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level. Key Features Design distributed systems that can be applied to real-world federated learning applications at scale. Discover multiple aggregation schemes applicable to...
Wiley-Scrivener, 2024. — 480 p. — ISBN: 978-1-394-21411-2. Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems....
Wiley-Scrivener, 2024. — 480 p. — ISBN: 978-1-394-21411-2. Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems....
Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango. — Chapman and Hall/CRC, 2025. — 192 р. — ISBN 978-87-7004-714-2. The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct...
Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango. — Chapman and Hall/CRC, 2025. — 192 р. — ISBN 978-87-7004-714-2. The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct...
Springer, 2024. — 355 р. — ISBN 978-3-031-66842-5. This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of...
Springer, 2024. — 355 р. — ISBN 978-3-031-66842-5. This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of...
Springer, 2024. — 355 р. — ISBN 978-3-031-66842-5. This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of...
Packt Publishing, 2021. — 270 p. — ISBN 978-1800567887. Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models...
Abdul Rahman, Christopher Redino, Sachin Shetty, Dhruv Nandakumar, Tyler Cody, Dan Radke. — Wiley-IEEE Press, 2025. — 288 p. — ISBN-13: 978-1394206452. A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration...
Abdul Rahman, Christopher Redino, Sachin Shetty, Dhruv Nandakumar, Tyler Cody, Dan Radke. — Wiley-IEEE Press, 2025. — 288 p. — ISBN-13: 978-1394206452. A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration...
Packt Publishing, 2019. — 252 p.— ISBN 1838828974, 9781838828974. Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step...
Packt, 2020. — 351 p. — ISBN: 9781839219061. Take a comprehensive and step-by-step approach to understanding machine learning Key Features Discover how to apply the scikit-learn uniform API in all types of machine learning models Understand the difference between supervised and unsupervised learning models Reinforce your understanding of machine learning concepts by working on...
Apress Media, LLC, 2025. — 346 p. — (Maker Innovations Series). — ISBN-13: 979-8-8688-1294-1. Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform. You’ll start by exploring Machine learning in the context of...
Apress Media, LLC, 2025. — 346 p. — (Maker Innovations Series). — ISBN-13: 979-8-8688-1294-1. Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform. You’ll start by exploring Machine learning in the context of...
Apress Media, LLC, 2025. — 346 p. — (Maker Innovations Series). — ISBN-13: 979-8-8688-1294-1. Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform. You’ll start by exploring Machine learning in the context of...
O’Reilly Media, 2025. — 416 p. — ISBN-13: 978-1-098-14924-6. Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and...
O’Reilly Media, 2025. — 416 p. — ISBN-13: 978-1-098-14924-6. Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and...
Packt Publishing, 2019. — 425 p. — ISBN: 978-1-83882-173-9. Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to...
Packt Publishing, 2019. — 425 p. — ISBN: 978-1-83882-173-9. Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to...
Springer, 2025. — 344 p. — (Cognitive Technologies). — ISBN 978-3-031-83096-9. This book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with...
Packt Publishing, 2020. — 490 p. — ISBN 9781800208919. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features Build, train, and deploy machine learning models quickly using Amazon SageMaker...
Packt Publishing, 2020. — 490 p. — ISBN: 9781800208919, 180020891X. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features - Build, train, and deploy machine learning models quickly using...
Packt, 2020. — 246 p. — ISBN: 9781838646486. Build secure private blockchain networks to handle mission-critical security challenges such as denial-of-service attacks, user wallets, and pool mining attacks Key Features Explore blockchain concepts such as cryptography, consensus algorithms, and security assumptions Architect network security for mission-critical decentralized...
Packt Publishing, 2020. — 500 p. — ISBN: 978-1-78961-121-2. Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and...
Packt Publishing Ltd., 2020. — 404 p. — ISBN: 978-1-78995-608-5. Use the power of deep learning with Python to build and deploy intelligent web applications When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you’ll cover the latest tools and technological practices that are being used to implement deep learning in web...
Packt Publishing Ltd., 2020. — 404 p. — ISBN: 978-1-78995-608-5. Use the power of deep learning with Python to build and deploy intelligent web applications When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you’ll cover the latest tools and technological practices that are being used to implement deep learning in web...
Packt Publishing Ltd., 2020. — 404 p. — ISBN: 978-1-78995-608-5. Use the power of deep learning with Python to build and deploy intelligent web applications When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you’ll cover the latest tools and technological practices that are being used to implement deep learning in web...
Packt Publishing, 2021. — 194 p. — ISBN 978-1800567641. Create better and easy-to-use deep learning models with AutoKeras Key Features Design and implement your own custom machine learning models using the features of AutoKeras Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis Get familiar with advanced concepts as multi-modal,...
Packt Publishing, 2021. — 194 p. — ISBN 978-1800567641. Create better and easy-to-use deep learning models with AutoKeras Key Features Design and implement your own custom machine learning models using the features of AutoKeras Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis Get familiar with advanced concepts as multi-modal,...
Packt Publishing, 2021. — 338 p. — ISBN 978-1800204492. Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods...
Packt Publishing, 2019. — 533 p. — ISBN: 978-1-78961-467-1. Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the...
CRC Press, 2025. — 264 p. — ISBN: 978-1-003-42590-8. This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences....
Wiley-IEEE Press, 2025. — 384 p. — ISBN-13: 978-1394294954. A practical guide to AI applications for Simple Python and MatLAB scripts Machine Learning and AI with Simple Python and MatLAB Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular...
Springer, 2025. — 548 p. — ISBN 978-981-97-5332-1. Conducting an in-depth analysis of Machine Learning, this book proposes three perspectives for studying Machine Learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the...
М.: ДМК Пресс, 2020. — 214 с. ISBN 978-5-97060-830-2 Сотруднику городской администрации Киёхара Кадзума поручено задание, которое без машинного обучения не выполнить. Под руководством своей давней знакомой Мияно Саяка он осваивает премудрости работы с искусственным интеллектом – от самых азов до глубокого обучения. Вместе с героями манги читатели узнают о том, что такое...
Пер. с англ. А. Слинкина. — Москва: ДМК Пресс, 2022. — 306 с. — ISBN 978-5-93700-125-2. Книга представляет собой подробный обзор передовых практик и паттернов проектирования в области прикладного машинного обучения. В отличие от многих учебников, уделяется внимание инженерным аспектам МО. Рассматриваются сбор, хранение и предобработка данных, конструирование признаков, а также...
М.: Манн, Иванов и Фербер, 2016. — 336 с. — ISBN: 978-5-00100-172-0. Популярная и интересная книга о поиске универсального самообучающегося алгоритма от ученого-практика. Алгоритмы управляют нашей жизнью. Они находят книги, фильмы, работу и партнеров для нас, управляют нашими инвестициями и разрабатывают новые лекарства. Эти алгоритмы все больше обучаются на основе тех массивов...
Пер. с англ. Ю. В. Ревича. – М.: ДМК Пресс, 2023. – 298 с.: ил. TinyML – технология, призванная расширить использование искусственного интеллекта за счет устройств с малым энергопотреблением, таких как микроконтроллеры. Прочитав эту книгу, вы сможете свободно использовать передовые практики и фреймворки ML в своей работе. Для начала вы ознакомитесь с основами развертывания...
М.: ДМК Пресс, 2018. — 252 с. Н20 - простая в использовании и открытая библиотека, которая поддерживает большое количество операционных систем и языков программирования, а также масштабируется для обработки больших данных. Эта книга научит вас использовать алгоритмы машинного обучения, реализованные в Н2О, с упором на наиболее важные для продуктивной работы аспекты. Рассмотрены...
М.: ДМК Пресс, 2022. — 500 с. Приложения машинного обучения демонстрируют лучшую эффективность при наличии обратной связи с человеком. Привлечение к работе подходящих сотрудников повышает точность моделей, уменьшает количество ошибок в данных, сокращает расходы и помогает ускорить выпуск моделей. В книге изложены методики эффективной совместной работы людей и машин. Здесь вы...
М.: ДМК Пресс, 2022. — 942 с. — ISBN 978-5-93700-119-1. Данный классический труд содержит обстоятельное современное введение в машинное обучение (включая глубокое обучение), рассматриваемое сквозь объединяющую призму вероятностного моделирования и байесовской теории принятия решений. Включен базовый математический аппарат (в т. ч. элементы линейной алгебры и теории...
М.: ДМК Пресс, 2017. — 418 с.: ил. — ISBN: 978-5-97060-409-0. Книга предоставит вам доступ в мир прогнозной аналитики и продемонстрирует, почему Python является одним из лидирующих языков науки о данных. Охватывая широкий круг мощных библиотек Python, в том числе scikit-learn, Theano и Keras, предлагая руководство и советы по всем вопросам, начиная с анализа мнений и заканчивая...
М.: Эксмо, 2022. — 509 с. История машинного обучения, от теоретических исследований 50-х годов до наших дней, в изложении ведущего мирового специалиста по изучению нейросетей и искусственного интеллекта Терренса Сейновски. Автор рассказывает обо всех ключевых исследованиях и событиях, повлиявших на развитие этой технологии, начиная с первых конгрессов, посвященных...
М.: Эксмо, 2022. — 509 с. История машинного обучения, от теоретических исследований 50-х годов до наших дней, в изложении ведущего мирового специалиста по изучению нейросетей и искусственного интеллекта Терренса Сейновски. Автор рассказывает обо всех ключевых исследованиях и событиях, повлиявших на развитие этой технологии, начиная с первых конгрессов, посвященных...
2-е издание. — М.: ДМК Пресс, 2025. — 272 с. — ISBN 978-5-93700-238-9. Изучение датамайнинга на примерах и упражнениях в этой книге позволит вам заглянуть за кулисы сложных методов и научиться эффективно работать с данными. По мнению автора, постигать датамайнинг и модели анализа данных лучше всего на практике, выполняя действия одно за другим. В отличие от большинства...
ДМК Пресс, 2019. - 436 с. пер. с англ. А.А. Слинкина. Машинное обучение – один из самых быстро развивающихся разделов инфор- матики с приложениями в самых разных областях. Цель этой книги – познакомить читателя с фундаментальными принципами машинного обучения и характерными для него алгоритмическими парадигмами. Книга содержит обширный свод осново- полагающих теоретических идей...
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