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...
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:...
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....
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....
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,...
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 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...
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...
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, 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...
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...
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. — 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, 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...
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. ●...
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...
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...
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...
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 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...
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...
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...
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...
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 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...
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...
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...
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...
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. — 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...
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...
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...
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...
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...
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. — 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,...
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...
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. — 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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
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,...
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, 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...
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...
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...
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. — 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. — 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 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...
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. — 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...
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...
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...
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...
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...
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. — 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...
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. — 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. — 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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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....
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...
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,...
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...
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...
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...
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, 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, 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...
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, 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, 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, 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...
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...
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...
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...
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,...
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...
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...
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...
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.
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....
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 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...
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...
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...
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...
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...
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...
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...
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 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...
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...
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. 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...
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...
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 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...
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...
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, 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...
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...
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...
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...
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...
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...
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,...
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...
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,...
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....
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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. — 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...
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