2018. — 75 p. — ASIN B07FKZN93N. How can a beginner approach machine learning with Python from scratch? Why exactly is machine learning such a hot topic right now in the business world? Ahmed Ph. Abbasi will lead you from being a complete beginner in learning a sound method of data analysis that uses algorithms, which learn from data and produce actionable and valuable...
Springer, 2023. — 235 р. — ISBN 978-3-031-18552-6. The Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modelling, and Applications presents the state of the art of the application of Machine Learning (ML) and Deep Learning (DL) in the domain of finance. We will present a combination of empirical evidence to diverse fields of finance so...
AI Sciences LLC, 2018. — 132 р. — ASIN B07GMVP3WP. Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the...
Springer, 2018. — 493 p. — ISBN: 978-3319735306. Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1...
Springer Cham, 2024. — 522 p. — eBook ISBN 978-3-031-53282-5. Simple and intuitive discussions of probability and statistics Discusses details of applications of mathematical concepts to machine learning Provides mathematical details without losing the reader in complexity This book covers probability and statistics from the machine learning perspective. The chapters of this...
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, 2021. — 204p. — (SpringerBriefs in Applied Sciences and Technology, SpringerBriefs in Computational Intelligence). — ISBN 978-981-33-4022-0. Network analysis has gained a lot of prominence over the past decade. This is because of a better understanding and control over learning representations of various entities like nodes, edges, subgraphs, cliques, and graphs that...
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. — 310 p. — ISBN B08LSLHBR8. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce,...
AI Publishing, 2020. — 308 p. — ISBN B08QJMNVCX. 10 Machine Learning Projects Explained from Scratch Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful...
O’Reilly, 2018. - 366p. - ISBN: 1491989386 This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data,...
Independently published, 2021. — 171 p. — ISBN 979-8724724272. Machine Learning is rapidly changing the world and it is the way of the Future of Human Technology. Art, information, processes, calculations, emotions will be rapidly learned and discover from machines. Do you want to learn more about the world of Machine Learning and its Applications? Would you like to improve and...
O’Reilly Media, 2018. — 216 p. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem,...
Packt, 2022. — 408 p. — ISBN 1801811911, 9781801811910. Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key Features Learn the applications of machine learning in biotechnology and life science sectors Discover exciting real-world applications of deep learning and natural language...
4th ed. — The MIT Press, 2020. — 712 p. — ISBN 9780262358064. A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new...
2nd edition. — The MIT Press, 2021. — 280 p. — (MIT Press essential knowledge). — ISBN 9780262542524. A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice...
IGI Global, 2024. —308 p. — eBook ISBN 9798369338841. Organizations worldwide grapple with the complexities of incorporating machine learning into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business...
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...
Apress, 2018. - 510p. - ISBN: 978-1484238721 Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book—Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an...
Orange Education Pvt Ltd, AVA, 2025. — 511 p. — ISBN 978-93-48107-13-8. true Preface Artificial Intelligence (AI) has revolutionized the way we interact with technology, shaping innovations across industries and redefining what machines are capable of achieving. From its theoretical roots to practical applications, AI continues to be a field that sparks curiosity and drives...
Independently published, 2024. — 92 p. This comprehensive guide is tailored to address precisely those concerns and assist you in navigating through the intricacies of Python Machine Learning. In "Python Machine Learning: A Comprehensive Beginner's Guide with Scikit-Learn and Tensorflow," you will embark on a journey to unravel the mysteries of: Understanding the essence of...
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...
CRC Press, 2023. — 310 p. This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on...
Apress Media LLC., 2020. — 262 p. — ISBN13: (electronic): 978-1-4842-5772-2. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine...
Apress, 2018. - 362p. - ISBN: 1484235630 Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a...
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...
Springer, 2023. — 768 p. — (Genetic and Evolutionary Computation) — eBook ISBN: 978-981-99-3814-8. Explores various ways evolution can help improve current methods of machine learning Presents real-world applications in medicine, robotics, science, finance, and other domains Serves as an essential reference for those interested in evolutionary approaches to machine learning...
Springer, 2023. — 327 p. — ISBN: 978-981-19-5169-5. Настройка гиперпараметров для машинного и глубокого обучения с помощью R: практическое руководство Hyperparameter tuning? Is this relevant in practice? Is it not rather an academic gimmick? This book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep...
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....
Wiley, 2014. — 408 p. — ISBN: 1118889061. Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML...
2nd Edition. — Wiley, 2020. — 420 p. — ISBN: 978-1-119-64225-1. Dig deep into the data with a hands-on guide to machine learning with updated examples and more! This book provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant,...
Gulf Professional Publishing, 2021. — 461 p. — ISBN 978-0128219294. Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves...
Springer, 2024. — 173 p. — (Springer INdAM Series 61). — ISBN 978-981-97-6768-7. Передовые методы оптимизации машинного обучения и обработки изображений In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of Machine Learning and imaging. The present book is a collection of recent contributions in the field of...
O’Reilly Media, 2016. — 82 p. — ISBN: 978-1-491-96297-8. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding...
Springer, 2018 - 257 p. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based...
Academic Press / Elsevier, 2023. — 348 p. — ISBN 978-0-323-96098-4. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and...
Packt Publishing, 2017. — 382 p. — ISBN: 978-1-78398-028-4. Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of...
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. ●...
2nd Edition. — Packt Publishing, 2018. — 300 p. — ISBN: 978-1788474399. Key Features Solve predictive modeling problems using the most popular machine learning Java libraries Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries Practical examples, tips, and tricks to help you understand applied machine learning in Java Book...
Springer, 2021. — 182 p. — (SpringerBriefs in Petroleum Geoscience & Engineering). — ISBN 978-3-030-71767-4. This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used...
Albert Bifet, Ricard Gavalda, Geoff Holmes, Bernhard Pfahringer. — MIT Press, 2018. — 288 p. — ISBN 978-0-262-03779-2. A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources―including sensor networks, financial markets, social...
Packt Publishing, 2019. — 168 p. — ISBN: 978-1838550356, 1838550356. This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Key Features Your handy guide to building...
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, 2019. — 386 p. — ISBN: 978-1789348279, 1789348277. Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to...
Packt Publishing, 2020. — 740 p. — ISBN: 978-1838820299 2nd.ed. Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time...
Packt Publishing, 2018. — 576 p. — ISBN: 978-1788621113. Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their...
2nd ed. — Packt Publishing, 2018. — 552 p. — ISBN: 1789347998. An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions...
Packt Publishing, 2017. — 449 p. — ISBN: 978-1785889622. Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide. About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering...
Springer, 2021. - 108p. - ISBN 9783030683788 This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in...
Springer, 2020. - 500p. - ISBN: 9783030457778 This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 418 p. — ISBN: 978‐1‐119‐56195‐8 (ebk). This book, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and...
Packt Publishing, 2015. — 190 p. — ISBN: 978-1-78439-908-5. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine...
Independently published, 2018. — 79 р. Do you want to impress the processes that you are working on? Do you want to make your machines more intelligent? If your answer to any of those questions is yes, then you have come to the right place. This book is a sequel to the book titled 'Machine Learning: A Step-by-Step guide.' In the first book, you gathered information on what...
Manning. 2016. 427 p. Eng. eal-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also...
O’Reilly, 2018. — 226 p. — ISBN: 1491976446. Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, youll first start to learn with regression modelling and then move into more advanced...
Andriy Burkov, 2019. — 160 p. — ISBN: 978-1-9995795-0-0. Update 2019-07-05 Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful...
Globaltech NTC, 2018. — 144 р. You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been...
O’Reilly Media, 2022. — 333 p. — ISBN-13: 978-1-492-08992-6. Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train...
Independently published, 2020. — 294 p. — ISBN B085PSKBRH. 3 Books in 1! Are you searching for the fastest way to discover the secrets of the fascinating world of Computer Science? For a very limited time, you have the opportunity to get three best-selling guides in a single phenomenal mega bundle: if you are a student or a professional looking for more technical skills, then...
Springer, 2023. — 295 p. — (Applied Mathematical Sciences). The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider's view of how to combine two mature fields, scientific computing and...
Independently published, 2022. — 98 p. — ASIN: B09QYQC31S. Have you thought about a career in data science? It’s where the money is right now, and it’s only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it’s the next logical step. Machine learning is a subsection...
Chapman and Hall/CRC, 2022. — 154 p. — ISBN 9781003267003. This book introduces the reader to Artificial Intelligence and its importance to our future. Campbell uses behavioural psychology, explores technology, economics, real-life and historical examples to predict five future scenarios with AI. Illustrating through speculative fiction, she describes possible futures after AI...
Mercury Learning & Information, 2020. — 261 p. — ISBN: 1683924703 As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several...
Mercury Learning & Information, 2020. — 339 p. — ISBN: 978-1-68392-467-8. This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures...
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...
Springer Cham, 2023. — 391 p. — (Statistics and Computing) — eBook ISBN: 978-3-031-41337-7. Presents the fundamental notions of supervised machine learning Provides a balance between the theory and applications of machine learning using Python, R, and Stata Fosters an understanding and awareness of machine learning methods over different software platforms This book presents...
Packt, 2021 — 344 p. — ISBN 1801076804, 9781801076807. Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key Features Get well-versed with DataRobot features using real-world examples Use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle Make use of...
Chapman & Hall, 2025. — 344 p. — eBook ISBN 978-1-003-29633-1. This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the...
Scrivener Publishing LLC., Wiley, 2022. — 470 p. — (Advances in Learning Analytics for Intelligent Cloud-IoT Systems). — ISBN 978-1-119-76225-6. Методы машинного обучения и аналитика для облачной безопасности This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine...
Independently published, 2021. — 225 p. — ASIN B09BNYDH5F. Have you always been curious about machine learning but do not know where to start. Or perhaps your new job requires you to learn machine learning but you are overwhelmed with all the information online. What is machine learning? What is Scikit-Learn? What does the fit() method that you see on so many online tutorials...
Manning Publications, 2023. — 335 p. — ISBN: 978-1617298042. Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your Machine Learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation...
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...
Wiley-IEEE Press, 2024. — 240 p. Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations. Machine Learning Applications discusses methodological advancements of Machine Learning and Deep Learning, presents applications in image processing, including face and vehicle detection, image...
Bentham Science Publishers, 2022. — 360 p. — ISBN 978-1-68108-940-9. Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include...
Academic Press/Elsevier, 2024. — 520 p. Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated...
Wiley, STE Ltd., 2022. — 255 p. — (Computer Science, Operational Research and Decision). — ISBN 978-1-78945-071-2. Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the...
Academic Press/Elsevier, 2023. — 300 p. — ISBN 978-0-12-824020-5. Состязательная устойчивость для машинного обучения Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image...
Springer Cham, 2024. — 119 p. — (Intelligent Systems Reference Library, volume 255). — eBook ISBN 978-3-031-59811-1. Presents the novel logo detection methods using machine learning paradigms Demonstrates the merits of the presented approaches over the reported approaches using the real-world applications Includes the state-of-the-art machine learning paradigms This book...
2nd edition. — Morgan & Claypool Publishers, 2018. — 284 p. — ISBN: 9781681733999. Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation:...
Packt Publishing, 2019. — 334 p. — ISBN: 9781789807943. Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains Implement smart...
Bentham Science Publishers, 2023. — 139 p. — eISBN: 978-981-5124-42-2. Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications,...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at...
Packt Publishing, 2017. — 376 p. — ISBN: 978-1788398435. Extract patterns and knowledge from your data in easy way using MatLAB. Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MatLAB Understand how your data works and identify...
Springer, 2021. — 173 p. — ISBN 978-3-030-70678-4. This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The...
CRC, 2020. — 430 p. — ISBN: 9781466510845. Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the...
Amazon.com Services LLC., 2020. — 151 p. Become the master of machine learning with this powerful guide. Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin? Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple,...
Packt Publishing, 2016. — 444 p. — ISBN: 978-1-78439-475-2. Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the...
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 H2O, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H2O, import and export data, and distinguish H2O algorithms. Readers will then...
O’Reilly Media, Inc., 2024. — 556 p. — ISBN 978-1-098-15601-5. Производственные системы машинного обучения: инженерные модели и конвейеры машинного обучения Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a...
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...
Independently Published, 2020. — 107 p. Are you looking for an easy to follow guide for learning Python programming for Machine Learning? In this book Marvin Dale is going to teach you the basics and some advanced tricks for learning Python and Machine Learning. "Machine Learning is the future". You have surely heard that sentence, from the news or from a friend. You also have...
Packt Publishing, 2017. — 442 p. — ISBN: 9781788295758. Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on...
Packt, 2019. — 360 p. — ISBN: 9781838645359. This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. Key Features Roll up your sleeves and start programming AI models No math, data science, or machine learning background required Packed with hands-on examples, illustrations, and clear step-by-step instructions 5...
Academic Press, 2023. — 326 p. — eBook ISBN: 9780128229057. Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple...
Springer, 2021. — 162 p. — ISBN 978-3-030-83212-4. This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting;...
Packt Publishing, 2017. — 438 p. — ISBN: 978-1788294041. An effective guide to using ensemble techniques to enhance machine learning models. Key Features Learn how to maximize popular machine learning algorithms such as random forests, decision trees, AdaBoost, K-nearest neighbor, and more Get a practical approach to building efficient machine learning models using ensemble...
Springer, 2020. — 573 p. — ISBN: 3030410676. This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of...
Orange Education Pvt Ltd, AVA, 2024. — 714 p. — ISBN 978-81-97651-20-5. Ultimate MLOps для моделей машинного обучения: используйте реальные практические примеры для эффективного создания, развертывания и масштабирования конвейеров машинного обучения с помощью MLOps The only MLOps guide you'll ever need. Key Features - Acquire a comprehensive understanding of the entire MLOps...
BPB Publications, 2023. — 150 р. — ISBN-13: 978-93-55511-133. A guide to understand the basics of Machine Learning and its application in the field of education. Key Features: - Create a more efficient and effective learning environment that meets the needs of all students. - Learn how to use the profound Machine learning advancements in the field of education. - Understand how...
Packt Publishing, 2020. — 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,...
Springer Singapore, 2023. — 295 p. — (Algorithms for Intelligent Systems) — eBook ISBN: 978-981-99-6645-5. Provides rich set of chapters of machine learning and metaheuristic optimization Emphasizes optimization algorithms such as PSO, ant colony optimization, cuckoo search algorithm, etc. Includes real-world examples with attention to theoretical aspects for better...
Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad (Editor). — Wiley, 2020. — 535 p. — ISBN: 978-1119654742. Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online...
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...
Pearson, 2018. — 456 p. — ISBN: 9353066697. Readers of this book will gain a thorough understanding of machine learning concepts. Not only students but also software professionals will find a variety of techniques with sufficient discussions in this book that cater to the needs of the professional environments. Technical managers will get an insight into weaving machine...
Data Sciences, 2017. — 136 p. — ISBN: 1985670968. The overall aim of this book is to help you understand the application of machine learning techniques with Python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what...
Springer, 2023. — 253 p. — (Data Science, Machine Intelligence, and Law 02). — ISBN 978-3-031-19666-9. This book gives a comprehensive overview of the state of Artificial Intelligence (AI), especially Machine Learning (ML) applications in public service delivery in Estonia, discussing the manifold ethical and legal issues that arise under both European and Estonian law. Final...
Springer, 2023. — 231 p. — (Computational Intelligence Methods and Applications). — ISBN 978-981-19-3887-0. Метаэвристика для машинного обучения: новые достижения и инструменты Using metaheuristics to enhance Machine Learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule...
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...
De Gruyter, 2024. — 316 p. — (De Gruyter Handbooks of Digital Transformation 1). The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to Artificial Intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on...
O’Reilly, 2020. — 166 p. — ISBN: 1492072745. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand...
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...
Apress, 2019. - 365p. - ISBN: 9781484236574 Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior...
2nd edition. — Boca Raton: CRC Press, 2025. — 242 p. A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of Machine Learning concepts from basic principles and illustrates every concept using examples in both Python and MatLAB, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter...
New York: Addison-Wesley Professional, 2019. — 592 p. The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyonewill help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is...
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to...
Amazon.com Services LLC, 2020. — 120 p. — ISBN: 979-8615125096. You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you’ve always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and...
Apress, 2024. — 478 p. — ISBN 9798868803536, ASIN B0CYNRV8BK. This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various...
Springer Cham, 2023. — 267 p. — eBook ISBN: 978-3-031-39477-5. Tackles the 'why' questions of data science and deep learning Interdisciplinary approach to model engineering Information measurements for MLOps, Data drift, bias This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the...
O’Reilly, 2023. — 360 p. — Second Edition. This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural...
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...
Монография 2018. - 442 с. На английском языке. Эта книга реализует много общих алгоритмов машинного обучения в эквивалентных R и Python. Это 3-е издание книги. В книге рассматриваются реализации R и Python различных регрессионных моделей, алгоритмы классификации, в том числе логистическая регрессия, классификация KNN, SVM, b-сплайны, случайный лес, форсирование и т. д. Далее в...
BPB Publications, 2021. — 218 p. — ISBN: 978-93-90684-700. Utilize Python and IBM Watson to put real-life use cases into production. Key Features Use of popular Python packages for building Machine Learning solutions from scratch. Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. Expert-led best practices to put...
Springer Singapore, 2024. — 304 p. — eBook ISBN 978-981-97-3954-7. Explains basic theories without over explanation for readers to build machine learning models Guides readers step by step to use KNIME to practice machine learning Provides examples from Kaggle competitions in various fields This book guides professionals and students from various backgrounds to use machine...
KJ Publishing, 2022. — 166 p. Machine Learning is a complex subject area. Our goal in this lesson is to introduce you to some of the most common terms and ideas used in Machine Learning. I will then walk you through the different steps involved in Machine Learning (ML) and finish with a series of examples that use Machine Learning to solve real-world situations. Machine...
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...
Second updated edition. — O’Reilly, 2019. — 856 p. — ISBN: 1492032646. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using...
2nd Edition. (3rd. Rev.) — O’Reilly, 2019. — 856 р. - ISBN: 1492032646 Final Edition Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this...
Independently published, 2019. — 613 p. — ISBN B07Y2H2M8R. 4 Books in 1! Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model and you have no programming skill? Do you know a bit of Python coding and want to learn more about how this deep learning works? This bundle is going to guide you to...
Springer, 2022. — 161 p. — ISBN 978-3-031-14634-3. Анализ данных с помощью машинного обучения для психологов: ускоренный курс по изучению Python 3 и машинного обучения за 10 часов The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of...
Addison-Wesley Professional, 2018. — 256 p. — ISBN: 9780134863924, 0134863860 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you...
Packt Publishing - ebooks Account, 2016. — 614 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689 This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working...
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...
Morgan Kaufmann, 2018. — 569 p. — ISBN 9780081006597. Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the...
The MIT Press, 2020. — 304 p. — ISBN 9780262358019. An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key...
O’Reilly, 2020. - 130p. - ISBN: ISBN: 9781492050124 If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data...
Morgan Kaufmann/Elsevier, 2024. — 408 p. Synthetic Data and Generative AI covers the foundations of Machine Learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including...
Manning Publications, 2020. — 319 p. In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through...
Springer, 2024. — 180 p. — (Machine Learning: Foundations, Methodologies, and Applications). — ISBN 978-981-97-0687-7. Today, Machine Learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may...
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...
Springer, 2022. — 78 p. — (SpringerBriefs in Computer Science). — ISBN 978-981-19-4873-2. Использование машинного обучения в программно-определяемых сетях Emerging Machine Learning (ML) techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of...
IGI Global, 2024. — 530 p. The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by Artificial Intelligence (AI) and Machine Learning (ML) technologies in the development of intelligent machines. As the demand for intelligent machines continues to rise across various sectors, understanding the...
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, 2021. — 293 p. — ISBN 978-3030712693. This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select...
Springer, 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. — 600 p. The past decade has witnessed the broad adoption of Artificial Intelligence and Machine Learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit,...
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...
Apress, 2017. — 204 p. Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time...
Orchid Publishing, 2023. — 431 p. Are you ready to dive into the fascinating world of Machine Learning and Artificial Intelligence? Do you want to understand the technology that powers everything from personalized recommendations to self-driving cars? If so, "Machine Learning With Python Programming : 2023 A Beginners Guide" is the book you've been waiting for. This...
O’Reilly, 2019. — 320 p. — ISBN: 1492047546. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for...
Independently published, 2025. — 100 p. Unlock the Power of Machine Learning with This Beginner-Friendly Guide! Are you ready to dive into the exciting world of Machine Learning but don’t know where to start? "Machine Learning for Beginners: A Step-by-Step Guide with Code Examples" is your perfect starting point! This book is designed for absolute beginners who are eager to...
Springer/Posts & Telecom Press, 2023. — 398 p. Firstly, it takes common competition scenarios as a guide by giving the main processes of using Machine Learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover,...
Springer, 2021. — 214 p. — (Lecture Notes in Mathematics 2293). — ISBN 978-3-030-77561-2. от геометрии к физике и машинному обучению The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to...
Packt Publishing, 2016. — 278 p. — ISBN: 978-1-78439-863-7. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum...
Apress, 2021. — 300 p. — ISBN-13 978-1-4842-7031-8. Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book...
Routledge, 2023. — 191 p. — ISBN: 978-1-032-07277-7. Машинное обучение для бизнес-аналитики: анализ данных в реальном времени для принятия решений Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data...
Walter de Gruyter GmbH, 2021. — 216 p. — ISBN 978-3-11-070248-4. Machine learning (ML) is a part of computerized reasoning which comprises algorithms and artificial neural networks and displays qualities firmly connected with human insight. The book focuses on the applications of ML for sustainable development. This book provides an understanding of sustainable development and...
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...
Springer, 2023. — 319 p. — (Artificial Intelligence: Foundations, Theory, and Algorithms). Machine Learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it...
Apress, 2018. — 339 p. Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data...
Amazon.com Services LLC, 2020. — 51 p. — ISBN: 979-8633574968. Are you prepared for the inevitable AI revolution? How can you use it in your current role as a business leader? Artificial intelligence has become the center of strategic decision making for organizations. It disrupts the way industries function – from sales and marketing to finance and HR, companies are betting on...
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...
The MIT Press, 2017. — 624 p. — ISBN: 026203641X. The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage,...
Packt Publishing, 2018. — 350 p. — ISBN: 1788996402. Power up your C# and .NET applications with exciting machine learning models and modular projects Key Features Produce classification, regression, association and clustering models Expand your understanding of machine learning and C# Get the grips of C# packages such as Accord.net, LiveCharts, Deedle Book Description Machine...
Packt Publishing, 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, 2016. — 298 p. — ISBN: 978-1-78588-660-7. Explore the web and make smarter predictions using Python Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps...
Amazon Digital Services LLC, 2019. — 135 р. — ASIN B07NRVL21R. There are different processes that are included within the meaning of the term "learning." If you refer to a dictionary and look up the meaning of learning, then you will come across different phrases like "to gain knowledge, understanding of or skill, through study, experience or instruction," and "the change in...
Packt Publishing, 2019. — 456 p. — ISBN: 1789136364. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind...
Packt, 2018. — 503 p. — ISBN: 178934641X The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to...
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...
Independently published, 2022. — 564 p. The goal of artificial intelligence is to program computer systems to make use of example information or past experience to address an offered trouble. Numerous successful applications of machine learning exist already, consisting of systems that evaluate past sales data to forecast consumer habits, enhance robotic habits so that a task...
Springer Singapore, 2023. — 329 p. — (Machine Learning: Foundations, Methodologies, and Applications) — eBook ISBN: 978-981-19-7584-4. Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics...
Springer, 2021. - 411p. - ISBN 9783030658991 This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine...
HiTeX Press;, 2024. — 403 p. — ASIN: B0DL4V4L2G. "Few-Shot Machine Learning: Doing More with Less Data" is an illuminating exploration into the cutting-edge techniques that enable machines to learn efficiently from limited data. This book delves deep into the domain of few-shot learning—a revolutionary approach that challenges the traditional dependency on vast datasets. By...
Packt, 2019. — 482 p. — ISBN: 1789952298. Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key Features Learn how to select the most suitable Python library to solve your problem Compare...
Packt Publishing, 2019. — 380 p. — ISBN: 978-1-78995-492-0. Explore the exciting world of machine learning with the fastest growing technology in the world Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply...
Packt, 2018. — 172 p. — ISBN: 1789343704. Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight from volumes of...
Packt Publishing, 2022. — 552 p. — ISBN 9781803246802. We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly...
2nd Edition. — Springer, 2023. — 279 p. The new edition of this popular professional book on Artificial Intelligence (ML) and Machine Learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective,...
Springer, 2023. — 137 p. — (Synthesis Lectures on Learning, Networks, and Algorithms). — ISBN 978-3-031-19066-7. Алгоритмы оптимизации для распределенного машинного обучения Stochastic gradient descent (SGD) is the backbone of supervised Machine Learning training today. Classical SGD was designed to be run on a single computing node, and its error convergence with respect to...
Packt Publishing, 2016. — 405 p. — ASIN B01BSTEDKY. Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range...
Packt Publishing, 2016. — 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...
Wiley-Scrivener, 2024. — 327 p. — (Серия: Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN 978-1-394-23392-2. Метаэвристика для машинного обучения: алгоритмы и приложения The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and...
Packt Publishing, 2016. — 258 p. — ISBN: 978-1-78439-658-9. Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning...
Packt Publishing, 2018. — 367 p. — ISBN: 978-1-78899-824-6. A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added...
Packt Publishing, 2017. — 420 p. — ISBN: 978-1-78728-074-8. Key Features Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Book Description Join...
O’Reilly, 2023. — 266 p. — (Sixth Early Release). Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. These systems treat...
O’Reilly Media, Inc., 2023. — 250 p. Whether based on academic theories or discovered empirically by humans and machines, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. Unlike...
Independently published, 2021. — 674 p. — ISBN B08V4VZSCH. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine...
O’Reilly Media, Inc., 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...
CRC Press, 2025. – 300 p. – ISBN 978-1-032-55232-3. This book presents the research into and application of Machine Learning in quantum computation, known as Quantum Machine Learning (QML). It presents a comparison of Quantum Machine Learning, classical Machine Learning, and traditional programming, along with the usage of quantum computing, toward improving traditional Machine...
2nd Edition. — Apress Berkeley, 2023. — 661 p. — eBook ISBN: 978-1-4842-9801-5. This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry...
Apress, 2018. — 355 p. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning...
IGI Global, 2020. — 324 p. — ISBN 9781799801832. Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and...
CreateSpace Independent Publishing Platform, 2018. — 210 p. — ISBN: 978-1986406857 (paperback). Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for...
Pearson, 2019. — 288 p. — ISBN: 9780134116556. Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers...
Boston: The MIT Press, 2015. - 624p.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the...
2nd.ed. - The MIT Press, 2020. - 856p. - ISBN 0262044692 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications...
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, 2021. — 296 p. — ISBN 978-1-4842-7149-0. Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a...
Springer, 2018. — 317 p. — ISBN: 978-3319730394. This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General...
CRC Press, 2024. — 362 p. — ISBN 978-1-032-47162-4, 978-1-032-47163-1, 978-1-003-38485-4. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become...
2nd ed. — Springer, 2017. — 348 p. This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees,...
3rd edition. — Springer, 2021. — 458 p. — ISBN 978-3-030-81934-7. This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more...
New York: Springer, 2020. — 202 p. This book discusses one of the major applications of Artificial Iintelligence (AI): the use of Machine Learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also...
Apress, 2020. — 146 p. — ISBN13: 978-1-4842-5939-9. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best...
BPB Publications, 2022. — 751 p. — ISBN 978-93-91030-42-1 Master the ML process, from pipeline development to model deployment in production. Key Features Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. A step-by-step approach to cover every data science task with utmost efficiency and highest performance. Access...
Springer, 2021. — 349 p. — ISBN 9789811594915. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these...
Packt Publishing, 2019. — 294 р. — ISBN: 978-1788830577. Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right...
Jamba Academy, 2023. — 623 p. Are you ready to dive into the world of Python Machine Learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of Machine Learning and the powerful Scikit-learn library. Key...
Artech House, 2025. — 329 p. — ISBN-13: 978-1-68569-033-5. Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification...
Packt, 2019. - 298p. - ISBN: 1789612853 Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in...
O’Reilly Media, 2021. — (Early Release). — ISBN 978-1-492-09079-3. Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde–Google Developer Expert in machine learningand the web–provides a...
O’Reilly Media, Inc., 2021. — 482 p. — ISBN 9781098102364. 2021-07-21: First Release By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including...
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...
O'Relly, 2021. — 156 р. — ISBN: 978-1098115715. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the...
No Starch Press, 2021. — 273 p. — ISBN 9781718500563. A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence....
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...
3rd ed. — Packt, 2019. — 458 p. — ISBN: 1788295862. Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by...
O’Reilly Media, 2020. — 232 p. — ISBN: 978-1-492-07277-5. Your company creates terabytes and even petabytes of data, but are you actually putting it to work? The majority of enterprises stumble on their way to becoming data driven. Machine learning promises to reverse that trend, and early adopters are already seeing the benefits with increased ROI. In this report, author Alice...
CRC Press, 2019. — 350p. - ISBN: 0815356226 Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and...
Oxford University Press, 2021. — 333 p. — ISBN 9780190941666. Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data science that requires only a few weeks to learn instead of years of training Though the concept of computers learning to solve a problem may still conjure thoughts of futuristic...
Packt Publishing, 2021. — 249 p. — ISBN 978-1-80056-079-6. Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow Use MLflow to iteratively develop a ML model and manage it Discover and work with the...
Wiley, 2021. — 216 p. — ISBN 978111968236. Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series...
Second Edition. — Independently published, 2021. — 281 p. — ISBN 978-1838132217. This Second Edition improves clarity of code, general formatting, text explanations as well as updating the AI for 2021 stock selection. Build Your Own AI Investor Breaks down Value Investing for the AI revolution, whilst being accessible to anyone, even if you've never invested in stocks or coded...
O’Reilly Media, Inc., 2023. — 290 p. — ISBN-13: 978-1-098-10665-2. With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph,...
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...
Springer, 2022. — 109 p. — (Wireless Networks). — ISBN 978-3-031-16374-6. This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing...
Springer, 2024. — 530 p. — ISBN 978-981-99-3916-9. This book provides a comprehensive and systematic introduction to the principal Machine Learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum...
Springer, 2023. — 302 p. — ISBN 978-3-031-35050-4. This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of...
Cham: Springer International Publishing, 2019. — 273 p. — ISBN: 978-3-319-98566-4. This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also...
BPB Publications, 2023. — 271 p. Learn how to use AutoML to leverage Machine Learning for solving business problems. Key Features - Get familiar with the common machine learning problems and understand how to solve them. - Understand the importance of different types of data and how to work with them effectively. - Learn how to use machine learning and AutoML tools to solve...
Springer, 2022. — 288 p. — ISBN 978-981-16-9839-2, 978-981-16-9840-8. Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained...
Springer, 2020. — 285 p. — ISBN: 978-981-15-2910-8 (eBook). This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is...
Packt Publishing, 2017. — 344 p. — ISBN: 978-1783553112. +Sample Files Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book...
3rd edition. — Packt Publishing, 2020. — 526 p. — ISBN: 1800209711, 978-1800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data...
Bentham Books, 2022. — 240 p. — ISBN 978-981-5079-19-7. This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it...
Scientific Books, 2024. — 126 p. In this book we will develop Machine Learning techniques related to non-linear regression. More specifically, we will go deeper into non-linear multiple regression models with all their identification, estimation and diagnosis problems. Special emphasis is placed on generalised linear models and all types of derived non-linear models: Logit...
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...
Springer, 2023. — 131 p. — (Springer Tracts in Advanced Robotics 156). — ISBN 978-3-031-37831-7. Индуктивные предубеждения в машинном обучении для робототехники и управления One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In...
Orange Education Pvt. Ltd , 2024. — 247 р. — ISBN 8197256373, 978-8197256370, ASIN B0D8L3Q283. Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it...
IGI Global, 2020. — 344 p. — (Advances in Computer and Electrical Engineering). — ISBN 978-1799830955. Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock...
Springer, 2021. — 349 p. — (Algorithms for Intelligent Systems). — ISBN 978-981-33-4045-9. This book comprises the best deliberations with the theme “Machine Learning Technologies and Applications” in the “International Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2020),” organized by the Department of Computer Science and Engineering, VNR...
Dr. Wasim Akram Mandal. — 2025. — 76 p. Artificial intelligence is evolving at an unprecedented pace, and new breakthroughs continue to reshape the way we interact with technology. While OpenAI's ChatGPT has dominated the AI space, a new contender has emerged—DeepSeek AI, an innovative and powerful language model that challenges the status quo. This book, Mastering DeepSeek AI:...
CRC Press. — 2025. — 361p. — (Chapman & Hall/CRC Artificial Intelligence and Robotics Series). — ISBN: 978-1-032-87013-7. Энергоэффективность и надежность передовых архитектур машинного обучения: межуровневый подход Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient...
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,...
Springer, 2022. — 254 p. This book introduces machine learning and its applications in smart environments/cities. At this stage, a comprehensive understanding of smart environment/city applications is critical for supporting future research. This book includes chapters written by researchers from different countries across the globe and identifies critical threads in research...
Wiley, 2019. — 236 p. — ISBN10: 1119548217, 13 978-1119548218. Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial...
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 p. Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The...
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...
Abiprod Pty, 2018. — 125 p. — ISBN: 1718095031. Are you excited about Artificial Intelligence and want to get started?Are you excited about Machine Learning and want to learn how to implement in Python? The book below is the answer. Given the large amounts of data we use everyday; whether it is in the web, supermarkets, social media etc. analysis of data has become integral to...
Apress, 2019. — 384 p. Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance,...
No Starch Press, 2024. — 272 p. — ISBN-13: 978-1718502109. Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the...
Apress, 2021. — 284 p. — ISBN 978-1484270226. Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights)....
Springer, 2018. — 373 p. — ISBN: 3319949888. This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental...
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...
Springer, 2024. — 259 p. — ISBN 978-3-031-56430-7. This book is for individuals with a scientific background who aspire to apply Machine Learning (ML) within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-64-0. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence...
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,...
Sybex, 2019. — 528 p. — ISBN: 1119556716. Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While...
Wiley & Sons, Inc., 2020. — 336 p. — ISBN: 978-1-119-60290-3 (ebk). Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence...
Independently published, 2021. — 93 p. — ASIN B0952D2T8C. The Machine Learning process is revolutionizing many sectors in engineering including manufacturing sectors such as the Friction Stir Welding Process. The implementation of machine learning algorithms in Friction Stir Welding process not only reduced the time of the experiment but also reduces the cost of the experiment....
Springer Cham, 2025. — 132 p. — (Synthesis Lectures on Engineering, Science, and Technology). — eBook ISBN 978-3-031-82010-6. Comprehensive guide to MLOps. Covers all aspects of the MLOps process. No prior knowledge of MLOps is required. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable...
Springer, 2018. — 93 p. — ISBN10: 331975713X; ISBN13: 978-3319757131. This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior. The author describes techniques to reduce significantly the time required for simulations involving...
Scrivener Publishing, 2021. — 528 p. — ISBN 978-1-119-78580-4. In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise...
2nd Edition. — The MIT Press, 2019. — 504 р. — (Adaptive Computation and Machine Learning series). — ISBN: 978-0262039406. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It...
Leanpub, 2023. — 216 p. Master Machine Learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your Machine Learning applications. Machine Learning is transforming fields from healthcare diagnostics to climate change predictions through their predictive performance. However, these complex Machine...
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...
Cham: Springer International Publishing, 2019. — 206 p. — ISBN: 978-3-319-99223-5. This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational...
Amazon Digital Services LLC, 2019. — 46 p. Order Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science, NumPy, Scikit Learn, Pandas and Tensorflow now to learn all the basic concepts you need to know about machine learning and Python. The purpose of...
Amazon Digital Services LLC, 2018. — 50 р. Excited about computer science, machine learning and programming? This book is the ultimate guide to Python programming for beginners and intermediates! Considering that Python is used by big tech companies like Google, Nokia, IBM, Yahoo!, Pinterest, Disney and many others, it makes a lot of sense to prioritize learning Python if you...
De Gruyter, 2023. — 506 р. — ISBN: 978-3-11-078612-5. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 478 р. — ISBN 978-3-11-078614-9. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 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...
2nd Edition. — John Wiley & Sons, 2021. — 464 p. — (For Dummies). Your comprehensive entry-level guide to machine learning. While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android?as in the movie Ex Machina?it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and...
Apress, 2021. — 192 p. — ISBN 1484265424, 9781484265420. Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application...
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...
Springer, 2023. — 101 p. — (SpringerBriefs in Computer Science). This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and...
Leanpub, 2022. — 112 p. Build a career while doing technical wriitng. I earned $300 for my first paid Data Science and Machine Learning article. I get paid between $250 and $500 for each Data Science article I write. In this ebook, I'll show you how you too, can earn while writing about Data Science and Machine Learning. You have been learning about Data Science and Machine...
Amazon Digital Services LLC, 2019. — 175 p. Python with machine learning is increasing day-to-day popularity. Python is currently a widely used programming language for machine learning. Like the use of python in professional work, it is also growing reputation in the academic sector. In Coursera, various online courses, like programming language for machine learning, the use...
BPB Publications, 2020. — 338 p. — ISBN 978-93-89845-365. An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and...
Packt Publishing, 2022. - 376 p. - ISBN 180324710X. Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level. Key Features Design distributed systems that can be applied to real-world federated learning applications at scale. Discover multiple aggregation schemes applicable to...
Apress, 2022. — 343 p. — ISBN 9781484278017, 9781484278024. Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms. You’ll begin by reviewing the theoretical aspects of machine learning...
Wiley-Scrivener, 2024. — 480 p. — ISBN 978-1-394-21411-2. Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As...
AI Sciences, 2018 — 190p. — ISBN13: 978-1724417503. This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests...
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...
Independently published, 2024. — 84 p. Do you find yourself unsure of how to apply your existing knowledge to Python? If you are a beginner programmer who wants to learn Python Machine Learning, this book is for you. This book will help you understand how to use Python to apply your existing skills to Machine Learning problems. Machine Learning is a fascinating and powerful...
Manning Publications, 2024. — 576 p. — (MEAP v9). How do you know what might have happened, had you done things differently? Causal Machine Learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. In Causal AI you will learn how to: Build causal...
Packt Publishing, 2018. - 378p. - ISBN: 1788838297 Leverage the power of Apple's Core ML to create smart iOS apps Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple, with APIs...
O’Reilly Media, 2022. — 563 p. — ISBN 978-1-098-11292-9. Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved. But first,...
Heidelberg: O’Reilly, 2018. — 183 S. — ISBN: 978-3-96009-052-6. Language: German Machine Learning erreicht beinahe alle Bereiche der Technik und der Gesellschaft. In diesem Buch bekommen Sie die schnellstmögliche Einführung in das äußerst umfangreiche Themengebiet des Machine Learning und der statistischen Datenanalyse. Dabei werden alle wesentlichen Themen abgedeckt und mit...
Apress, 2021. — 192 p. — ISBN 978-1484271094. Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern...
Amazon Digital Services LLC, 2018. — 54 р. — (Machine Learning for Beginners Book 1). Welcome to the world of machine learning! Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core...
Apress Media LLC., 2020. — 570 p. — ISBN13: (electronic): 978-1-4842-5174-4. Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most...
Wiley, 2020. — 464 p. — ISBN: 1119591511. Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning―a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions―allows organizations to...
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...
Amazon Digital Services LLC, 2018. — 129 p. — ASIN B07F2NYDTH. Smart homes, self-driving cars, Siri, Alexa - some typical examples of how machine learning and artificial intelligence have become part of our daily life. Wouldn't it be cool to understand the concepts behind these complex topics? This book teaches you how to integrate machine learning into your apps. We're going...
Springer, 2021. — 170 p. — ISBN 978-3-030-74041-2. This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing...
Springer, 2021. — 769 p. — ISBN 978-3-030-70541-1. This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and...
O’Reilly Media, 2018. — 209 p. — ISBN10: 149199584X, 13 978-1491995846. EPUB (True/HQ) Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook,...
Manning Publications, 2022. — 358 р. — ISBN: 978-1617299797. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This...
Apress Media LLC, 2022 — 347 p. — ISBN-13: 978-1-4842-7921-2. Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep...
Apress, 2017. — 382 p. — ISBN10: 1484222490, 13 978-1484222492 This book is a comprehensive guide to machine learning with worked examples in MatLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews...
Apress, 2019. — 347 p. — ISBN: 1484239156. Harness the power of MatLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MatLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses...
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...
Springer, 2021. — 178 p. — (Studies in Big Data, 87). — ISBN 978-981-33-6517-9. This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning,...
Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli. — Apress Media LLC., 2020. — 320 p. — ISBN13: (electronic): 978-1-4842-6094-4. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow...
2nd edition, Apress, 2021. — 428 p. — ISBN 148426536X, 9781484265369. This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine...
Apress, 2019. — 368 р. — ISBN: 978-1-4842-3799-1. Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data...
Independently published, 2021. — 201 p. Introduction. What Is Machine Learning? Machine Learning - Concepts & Terms. Linear Regression with Python. Lists In Python. Modules In Python. Machine Learning Training Model. Conditional or Decision Statements. Essential Libraries for Machine Learning in Python. What is the TensorFlow Library. Artificial Neural Networks. Conclusion.
O’Reilly, 2019. — 362 p. — ISBN: 1492035645. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled...
O’Reilly Media, 2021-12-10. — 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: 978-3-96088-877-2. Ein Großteil der weltweit verfügbaren Daten ist ungelabelt. Auf diese nicht klassifizierten Daten lassen sich die Techniken des Supervised Learning, die im Machine Learning viel genutzt werden, nicht anwenden. Dagegen kann das Unsupervised Learning - auch unüberwachtes Lernen genannt - für ungelabelte Datensätze...
Packt Publishing, 2019. — 342 р. — ISBN: 978-1788996921. This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques....
O’Reilly Media, 2021. — 304 p. — ISBN 1492053279, 9781492053279. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads–a process Kubeflow makes much easier. This practical book shows data scientists, data...
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...
Birkhäuser Cham, 2022. — 152 p. — ISBN 978-3-030-98316-1 This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization...
Springer, 2021. — 197 p. — ISBN 978-3-030-72068-1. This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical...
Springer, 2022. — 630 p. — (Communications and Control Engineering). — ISBN 978-3-030-95860-2. This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power...
Packt, 2022. — 440 p. — ISBN 1801072167, 9781801072168. Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building,...
Packt Publishing, 2024. — 603 p. — ISBN 978-1-80512-250-0. The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI, 2nd Edition / Справочник архитектора решений для машинного обучения: Практические стратегии и рекомендации по жизненному циклу ML, системному проектированию, MLOps и...
Code Planet, 2025. — 270 p. Machine Learning with Python in this all-in-one guide designed for beginners and experienced developers alike! Whether you're diving into supervised and unsupervised learning, exploring neural networks, or mastering real-world applications, this book provides step-by-step explanations, hands-on examples, and expert insights. Machine Learning (ML) is...
Final Release: O’Reilly Media, 2023. — 191 p. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you. Using Spark as...
CRC Press, 2024. — 226 p. — ISBN 978-1-032-47841-8, 978-1-032-48399-3, 978-1-003-38884-5. This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and...
Apress Media LLC, 2024. — 984 p. — ISBN-13: 979-8-8688-1023-7. This book is the first comprehensive guide to the intersection of data governance and Machine Learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data is central to their success, which gives ML data governance unprecedented significance. However, adapting data...
O’Reilly Media, 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,...
First Release. — O’Reilly Media, 2022. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure,...
O’Reilly Media, Inc., 2023. — 376 р. — ISBN 978-1-098-12027-6. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce...
Xlibris US, 2021. — 556 р. This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised Machine Learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector...
O’Reilly Media, 2023. — 380 p. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run...
Apress, 2021. — 127 p. — ISBN 978-1912807130. Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an...
Apress, 2020. — 355 p. — ISBN: 1484256689. Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation...
Springer Cham, 2023. — 451 p. — (Computational Methods in Engineering & the Sciences) — eBook ISBN: 978-3-031-36644-4. Comprehensive state-of-the-art book on scientific machine learning approaches in modelling & simulation Covers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modelling & material design Focuses on engineering applications in...
Springer, 2023. — 312 p. — (Industrial and Applied Mathematics). — ISBN 978-981-19-6552-4. This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the...
Packt Publishing, 2021. — 270 p. — ISBN 978-1800567887. Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models...
Springer, 2022. — 464 p. — ISBN 978-3-030-96756-7 This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML...
Abdul Rahman, Christopher Redino, Sachin Shetty, Dhruv Nandakumar, Tyler Cody, Dan Radke. — Wiley-IEEE Press, 2025. — 288 p. — ISBN-13: 978-1394206452. A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration...
Packt Publishing, 2019. — 502 p. — ISBN: 978-1838556334 (paperback). Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. Key Features Study supervised learning algorithms by using real-world datasets Fine tune optimal...
Leanpub, Sebastian Raschka, 2023. — 231 p. Have you recently completed a Machine Learning or Deep Learning course and wondered what to learn next? With 30 questions and answers on key concepts in Machine Learning and AI, this book provides bite-sized bits of knowledge for your journey to becoming a Machine Learning expert. Expand Your Machine Learning Knowledge Machine Learning...
Packt Publishing, 2016. — 1043 p. — ISBN: 978-1-78712-857-6. Leverage benefits of machine learning techniques using Python. Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of...
3rd edition. — Packt Publishing, 2019. — 771 p. — ISBN 9781789955750. Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of...
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...
IGI Global, 2017 - 129 p. Software development and design is an intricate and complex process that requires a multitude of steps to ultimately create a quality product. One crucial aspect of this process is minimizing potential errors through software fault prediction. Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities is an innovative...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Packt Publishing, 2017. — conv 1276 p. — ASIN B076CRXB76. Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Final version! Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of...
Manning Publications, 2020. — 528 p. — ISBN: 978-1617296574. Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools...
CreateSpace Independent Publishing Platform, 2017. — 56 p. The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple.From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept...
Lioncrest Publishing, 2021. — 222 p. — ISBN 978-1-5445-1882-4. How can you successfully deploy AI? When AI works, it’s nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren’t...
New York: amazon.com Services LLC, 2021. — 412 p. — ISBN B08SCQ2Y75. Machine learning can be broadly classified into supervised and unsupervised learning. By definition, the term supervised means that the “machine” (the system) learns with the help of something—typically a labeled training data. Training data (or a dataset ) is the basis on which the system learns to infer. An...
2nd Edition. — World Scientific Publishing Co. Pte. Ltd., 2019. — 302 p. — ISBN 978-981-120-195-0. Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition) (Series in Machine Perception and Artificial Intelligence) 2nd Edition This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories,...
CRC Press, 2021. — 221 p. — ISBN 9780367703868. Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This...
Wiley, 2022. — 464 p. — (Wiley Finance series). — ISBN 9781119791386 State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
USA: IGI Global, 2024. — 557 p. — (Advances in Information Security, Privacy, and Ethics (AISPE) Series). — ISBN 9798369341599. Машинное обучение и криптографические решения для защиты данных и сетевой безопасности As the Editors of this comprehensive reference book, Machine Learning and Cryptographic Solutions for Data Protection and Network Security, we are delighted to...
BPB Publications, 2021. — 866 р. This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms...
Business Expert Press, LLC, 2025. — 264 p. — ISBN-13 978-1-63742-749-1. EPUB (true) Machine Learning Fundamentals provides a comprehensive overview of Data Science, emphasizing Machine Learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed. Topics...
Packt, 2019. — 252 p. — ISBN: 1838828974. Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step approach to improving...
Springer, 2022. — 128 p. This book discusses the Versatile Video Coding (VVC), the ISO and ITU state-of-the-art video coding standard. VVC reaches a compression efficiency significantly higher than its predecessor standard (HEVC) and it has a high versatility for efficient use in a broad range of applications and different types of video content, including Ultra-High Definition...
Packt, 2020. — 351 p. — ISBN: 9781839219061. Take a comprehensive and step-by-step approach to understanding machine learning Key Features Discover how to apply the scikit-learn uniform API in all types of machine learning models Understand the difference between supervised and unsupervised learning models Reinforce your understanding of machine learning concepts by working on...
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.
Springer Singapore, 2024. — 127 p. — (Studies in Computational Intelligence, volume 1155). — eBook ISBN 978-981-97-2720-9. Covers three primary data types: numerical, textual, and image data Offers GitHub source code encompassing fundamental components and advanced machine learning tools Serves as a reference for researchers, students, practitioners, and policymakers Employing...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner....
Packt Publishing, 2019. — 327 р. — ISBN 978-1-78913-660-9. Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble...
O’Reilly Media, Inc., 2024. — 329 p. — ISBN: 978-1-492-09452-4. Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In...
Packt, 2019. - 664p. - ISBN: 9781838827878 Your one-stop guide to working with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js A handy guide to make your web applications smart from scratch using example-based approach Use flexible and...
CRC Press, 2025. — 222 p. — (Data Science Series). Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and Machine Learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An...
Orange Education Pvt Ltd, AVA, 2024. — 411 p. — ISBN 978-81-97223-94-5. Максимальное машинное обучение с Scikit-Learn: раскройте возможности Scikit-Learn и Python для создания передовых приложений прогнозного моделирования и более глубокого понимания машинного обучения Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. “Ultimate...
Springer, 2025. — 344 p. — (Cognitive Technologies). — ISBN 978-3-031-83096-9. This book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with...
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...
Wiley-Scrivener, 2024. — 489 p. The book provides a comprehensive overview of current research and developments in the field of Deep Learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of Deep Learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep Learning helps...
Springer, 2023. — 175 p. — (Engineering Optimization: Methods and Applications). — ISBN 978-981-99-7455-9. This book aims to provide a collection of state-of-the-art scientific and technical research papers related to Machine Learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering...
2nd Edition. — Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce. — Wiley, 2023. — 611 p. — ISBN 978-1119903833. MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,...
Oxford University Press, 2022. — 401 p. — ISBN 978–0–19–284529–0. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders...
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. — 585 p. Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using Machine Learning and Deep Learning techniques, all implemented with the power of Python and a...
2nd Edition. — Balige Publishing, 2023. — 525 p. In this project, we will perform an analysis and prediction task on stroke data using Machine Learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to...
2nd Edition. — Balige Publishing, 2023. — 355 p. Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it offers a wide range of...
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, 2020. — 446 p. — ISBN: 1484258010. Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from...
Packt Publishing, 2020. — 490 p. — ISBN: 9781800208919, 180020891X. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features - Build, train, and deploy machine learning models quickly using...
Packt, 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...
Apress, 2020. - 258p. - ISBN: 9781484262214 Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load...
Apress, 2020. - 150p. - ISBN 1484265459 This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models....
O’Reilly Media, 2023. — 450 р. — ISBN 978-1-098-12020-7. Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can...
Independently published, 2019. — 128 р. — ISBN: 1092554556. A baby learns to crawl, walk and then run. we are in the crawling stage when it comes to appllying Machine Learning. Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that makes...
Manning Publications, 2018. — 256 p. — ISBN: 978-1-6172-9333-7. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
Manning Publications, 2023. — 550 p. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including. Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for...
Manning Publ., 2023. — 168 p. Develop a mathematical intuition for how Machine Learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field...
AI Sciences LLC, 2018. — 184 p. — ASIN B07G4M16JF. Are you thinking of learning more about Machine Learning using Python? This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you...
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,...
IGI Global, 2020. — 674 p. — ISBN 1522596437. As todays world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to...
Wiley-Scrivener Publishing, 2021. — 305 p. — ISBN 978-1-119-76885-2. Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine...
IGI Global, 2024. — 327 p. — (Advances in Computational Intelligence and Robotics (ACIR) Book Series). — ISBN 979-8-3693-5271-7. In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative...
IGI Global, 2024. — 315 p. In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of...
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...
2nd edition. — CRC Press, 2023. — 498 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — eBook ISBN 978-1-003-26487-3. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic...
2nd edition. — CRC Press, 2023. — 866 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN 978-1-003-26487-3. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic...
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. This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power...
Springer, 2021. — 147 p. — (Terrorism, Security, and Computation). — ISBN 3030606139, 9783030606138. This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram’s behavior and reduce attacks...
The MIT Press, 2022. — 325 p. — ISBN 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard Machine Learning (ML) techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely...
CreateSpace, 2017. — 266 p. Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Springer, 2019. — 155 p. — ISBN: 981133028X This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis....
Apress, 2021. — 193 p. — ISBN 978-1484268421. Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable...
Springer, 2023. — 249 p. Master the art of Machine Learning and Data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the...
Springer, 2021. — 260 p. — ISBN 978-981-15-7876-2. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a...
Springer, 2023 p. — 353 p. — (Lecture Notes in Energy 44). — ISBN 9783031162480. Машинное обучение и его применение к реагирующим потокам: машинное обучение и сжигание This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of...
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...
2nd ed. — Apress, 2019. — 469 p. — ISBN: 1484249461. Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts...
CRC Press, 2025. — 171 p. — (Innovations in Multimedia, Virtual Reality and Augmentation). — ISBN 978-1-032-76147-3. Машинное обучение в мультимедиа: раскрытие потенциала визуального и слухового интеллекта This book explores the interdisciplinary nature of Machine Learning in multimedia, highlighting its intersections with fields such as computer vision, natural language...
Emerald Publishing, 2021. — 479 p. — ISBN 978-1-80043-882-8. Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer...
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,...
K.Taylor, 2017. — 413 p. Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases....
W. W. Norton & Company, 2024. — 176 p. — (A Norton Short). In the industrial age, automation came for the shoemaker and the seamstress. Today, it has come for the writer, physician, programmer, and attorney. Literary Theory for Robots reveals the hidden history of modern machine intelligence, taking readers on a spellbinding journey from medieval Arabic philosophy to visions of...
Independently published, 2020. — 302 p. — ISBN B08NZW37N6. 4 Books in 1! Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading...
Packt Publishing, 2018. — 566 p. — ISBN: 1788390040. Practical, hands-on solutions in Python to overcome any problem in Machine Learning Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to...
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...
2nd Edition. — Academic Press, 2020. — 1160 p. — ISBN 9780128188033. Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods,...
Leanpub, Giannis Tolios, 2022. — 107 p. Упрощение машинного обучения с помощью PyCaret: подход с минимальным кодом для начинающих и экспертов! A beginner-friendly introduction to Machine Learning (ML) with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of Artificial Intelligence (AI), by easily training and deploying...
O’Reilly Media, Inc., 2024. — 345 p. — ISBN 978-1-098-15161-4. What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that...
Oxford University Press, 2020. — 272 p. — ISBN: 978-0-19-256309-5. Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and...
Packt Publishing, 2017. — 570 p. — ISBN: 9781785280511. Building Machine Learning applications with R. Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the...
Packt, 2019. — 426 p. — ISBN: 1789534143. Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language...
Packt, 2019. - 346p. - ISBN: 9781789614671 Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and...
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...
AI Publishing LLC, 2020. — 302 p. — ISBN: 978-1-7347901-5-3. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare,...
Wiley-IEEE Press, 2025. — 384 p. — ISBN-13: 978-1394294954. A practical guide to AI applications for Simple Python and MatLAB scripts Machine Learning and AI with Simple Python and MatLAB Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular...
Springer, 2024. — 365 p. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of Machine Learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind...
Leanpub, 2019. — 133 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
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...
Packt, 2023. — 905 p. Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working...
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...
Manning Publications, 2023. — 374 p. Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Unsupervised Learning with Generative AI you’ll learn: Fundamental building blocks and concepts of Machine Learning and unsupervised learning Data...
Apress Media LLC., 2020. — 392 p. — ISBN13: (electronic): 978-1-4842-6156-9. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as...
Apress, 2019. — 637 p. — ISBN: 1484253159. Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data...
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,...
2nd Edition. — Springer, 2022. — 476 p. — ISBN 978-3-031-06649-8. This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the...
Packt, 2020. — 311 p. — ISBN: 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment. Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and tricks and gain...
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...
Packt, 2022. — 542 p. — ISBN 1803241675, 9781803241678. Explore supercharged machine learning techniques to take care of your data laundry loads Key Features Learn how to prepare data for machine learning processes Understand which algorithms are based on prediction objectives and the properties of the data Explore how to interpret and evaluate the results from machine learning...
John Wiley & Sons, 2023. — 823 p. — ISBN 9781119817505. There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning...
Springer, 2025. — 548 p. — ISBN 978-981-97-5332-1. Conducting an in-depth analysis of Machine Learning, this book proposes three perspectives for studying Machine Learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the...
Elsevier, 2023. — 254 p. — ISBN 978-0-323-96126-4. Машинное обучение для транспортных исследований и приложений Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in Machine Learning provide new methods to tackle challenging...
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...
Apress, 2018. - 392p. - ISBN: 1484239504 Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You...
Independently Published, 2019. — 98 p. — ASIN B082MPL2Z2. One of the most widely recognized AI methods utilized for handling huge information is AI, a self-versatile calculation that shows signs of improvement examination and examples with experience or with recently included information. In the event that a computerized installment organization needed to identify the event or...
Independently published, 2020. — 335 p.— ISBN B08RCSJ39K. Mastering Deep Learning with Python: 2 Manuscripts This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and...
CRC Press, 2020. — 204 p. — ISBN13: 978-1-138-32822-8. In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have...
Wiley-IEEE, 2023. — 283 p. Extensive Resource for Understanding Key Tasks of Network and Security Management. AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly...
Packt Publising, 2015. — 405 p. — ISBN13: 978-1783982042. About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset from the churn dataset,applying different classification methods. Who This Book Is For If you want to learn how to use R for machine...
Wiley-IEEE Press, 2024. — 275 p. — ISBN 978-1-394-18852-9. Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively;...
Springer Singapore, 2023. — 133 p. — eBook ISBN: 978-981-99-7657-7. Presents state-of-the-art dirty data processing techniques for use in data pre-processing Opens promising avenues for the further study of dirty data processing Offers valuable take-away suggestions on dirty data processing for machine learning tasks In both the database and machine learning communities, data...
Apress, 2020. — 223 p. — ISBN: 978148425982-5. Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel...
Springer International Publishing AG, 2018. — 482 p. — (Human–Computer Interaction Series). — eBook ISBN 978-3-319-90403-0. With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of...
Springer, 2021. — 460 p. — ISBN 978-981-15-1966-6. An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and...
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...
СПб.: Питер, 2018. — 585 c. — ISBN: 978-5-496-03068-7. Книга "Python Data Science Handbook" - это подробное руководство по самым разным вычислительным и статистическим методам, без которых немыслима любая интенсивная обработка данных, научные исследования и передовые разработки.
М.: Эксмо, 2022. — 509 с. История машинного обучения, от теоретических исследований 50-х годов до наших дней, в изложении ведущего мирового специалиста по изучению нейросетей и искусственного интеллекта Терренса Сейновски. Автор рассказывает обо всех ключевых исследованиях и событиях, повлиявших на развитие этой технологии, начиная с первых конгрессов, посвященных...
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