An Introduction To Machine Learning
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An Introduction To Machine Learning
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Author : Gopinath Rebala
language : en
Publisher: Springer
Release Date : 2019-05-07
An Introduction To Machine Learning written by Gopinath Rebala and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-07 with Technology & Engineering categories.
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 with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any priorknowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.
Introduction To Machine Learning Fourth Edition
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Author : Ethem Alpaydin
language : en
Publisher: MIT Press
Release Date : 2020-03-24
Introduction To Machine Learning Fourth Edition written by Ethem Alpaydin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-24 with Computers categories.
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 technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.
An Introduction To Machine Learning
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Author : Miroslav Kubat
language : en
Publisher: Springer
Release Date : 2017-08-31
An Introduction To Machine Learning written by Miroslav Kubat and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-31 with Computers categories.
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, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Artificial Intelligence
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Author : RICHARD E.. JIANG NEAPOLITAN (XIA.)
language : en
Publisher: CRC Press
Release Date : 2020-06-30
Artificial Intelligence written by RICHARD E.. JIANG NEAPOLITAN (XIA.) and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-30 with categories.
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update retains the same accessibility and problem-solving approach, while providing new material and methods, including neural networks and deep learning.
Introduction To Machine Learning With Python
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Author : Andreas C. Müller
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-09-26
Introduction To Machine Learning With Python written by Andreas C. Müller and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-26 with Computers categories.
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. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
Machine Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Roland Bind
Release Date : 2018-09-07
Machine Learning For Beginners written by Steven Cooper and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-07 with Computers categories.
If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. ★★ Grab your copy today and learn ★★ ♦ The different types of learning algorithm that you can expect to encounter ♦ The numerous applications of machine learning ♦ The different types of machine learning and how they differ ♦ The best practices for picking up machine learning ♦ What languages and libraries to work with ♦ The future of machine learning ♦ The various problems that you can solve with machine learning algorithms ♦ And much more... Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish. Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know about Machine Learning!
Introduction To Machine Learning With Python
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Author : William Gray
language : en
Publisher: Independently Published
Release Date : 2019-05-04
Introduction To Machine Learning With Python written by William Gray and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-04 with categories.
What exactly is machine learning and why is it so valuable in the online business ? Are you thinking of learning Python machine learning ?This book teach well you the practical ways to do it ! ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★ Machine Learning is a branch of AI that applied algorithms to learn from data and create predictions - this is important in predicting the world around us. Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields. Today, it is a top skill in high demand in the job market. Machine learning has become an integral part of many commercial applications and research projects. Using Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. Inside Introduction to Machine Learning with Python, you'll learn: Fundamental concepts and applications of machine learning Understand the various categories of machine learning algorithms. Some of the branches of Artificial Intelligence The basics of Python Concepts of Machine Learning using Python Python Machine Learning Applications Machine Learning Case Studies with Python The way that Python evolved throughout time And many more Throughout the recent years, artificial intelligence and machine learning have made some enormous, significant strides in terms of universal, global applicability. You'll discover the steps required to develop a successful machine-learning application using Python. Introduction to Machine Learning with Python is a step-by-step guide for any person who wants to start learning Artificial Intelligence - It will help you in preparing a solid foundation and learn any other high-level courses. Stay ahead and make a choice that will last... If You like to know more, scroll to the top and select " BUY NOW " buttom ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★
A Hands On Introduction To Machine Learning
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Author : Chirag Shah
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-29
A Hands On Introduction To Machine Learning written by Chirag Shah and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-29 with Computers categories.
Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Introduction To Machine Learning
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Author : Yves Kodratoff
language : en
Publisher: Morgan Kaufmann
Release Date : 1988
Introduction To Machine Learning written by Yves Kodratoff and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computers categories.
A textbook suitable for undergraduate courses in machine learning and related topics, this book provides a broad survey of the field. Generous exercises and examples give students a firm grasp of the concepts and techniques of this rapidly developing, challenging subject. Introduction to Machine Learning synthesizes and clarifies the work of leading researchers, much of which is otherwise available only in undigested technical reports, journals, and conference proceedings. Beginning with an overview suitable for undergraduate readers, Kodratoff establishes a theoretical basis for machine learning and describes its technical concepts and major application areas. Relevant logic programming examples are given in Prolog. Introduction to Machine Learning is an accessible and original introduction to a significant research area.
A Concise Introduction To Machine Learning
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Author : A.C. Faul
language : en
Publisher: CRC Press
Release Date : 2025-05-14
A Concise Introduction To Machine Learning written by A.C. Faul and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-14 with Computers categories.
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 concludes with exercises to explore the content. The emphasis of the book is on the question of Why—only if “why” an algorithm is successful is understood, can it be properly applied and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities of methods, aims to give a thorough and in-depth treatment and develop intuition for the inner workings of algorithms, while remaining concise. This useful reference should be essential on the bookshelf of anyone employing machine learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable.