Introduction And Fundamental Concepts Of Machine Learning

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Understanding Machine Learning

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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19

Understanding Machine Learning written by Shai Shalev-Shwartz 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 2014-05-19 with Computers categories.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Hands On Machine Learning With R

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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07

Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Fundamentals Of Machine Learning

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Author : Thomas P. Trappenberg
language : en
Publisher:
Release Date : 2020

Fundamentals Of Machine Learning written by Thomas P. Trappenberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.

Interest in machine learning is exploding across the world, both in research and for industrial applications. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to both students and researchers.

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.

Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition

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Author : John D. Kelleher
language : en
Publisher: MIT Press
Release Date : 2020-10-20

Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition written by John D. Kelleher and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-20 with Computers categories.

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 including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Machine Learning

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Author : RODRIGO F MELLO
language : en
Publisher: Springer
Release Date : 2018-08-01

Machine Learning written by RODRIGO F MELLO and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-01 with Computers categories.

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 concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.

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.

Understanding The Fundamentals Of Machine Learning And Ai For Digital Business

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Author : Andy Ismail
language : en
Publisher: Asadel Publisher
Release Date : 2023-06-04

Understanding The Fundamentals Of Machine Learning And Ai For Digital Business written by Andy Ismail and has been published by Asadel Publisher this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-04 with Computers categories.

"Understanding the Fundamentals of Machine Learning and AI for Digital Business" is a comprehensive guide that provides a solid foundation in the concepts and applications of machine learning and artificial intelligence. This book covers a wide range of topics, from the history and understanding of machine learning to its purpose and application in the digital business landscape. Starting with the basics, readers will gain a clear understanding of supervised learning, unsupervised learning, and reinforcement learning. They will explore evaluation methods such as accuracy, precision, recall, F1 score, and ROC-AUC, and learn how to assess the performance of machine learning models. The book delves into regression analysis, covering important techniques like polynomial regression, ridge regression, lasso regression, and vector regression. It also explores classification methods, including Naive Bayes, K-Nearest Neighbors (KNN), decision trees, random forest, and support vector machines. Readers will gain insights into clustering techniques like K-means, hierarchical clustering, and density-based clustering. They will also explore the fascinating world of deep learning, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), and natural language processing (NLP) techniques like tokenization, stemming, and lemmatization. The book provides practical exercises throughout, allowing readers to apply their knowledge and reinforce their understanding. It covers topics such as dealing with violations of assumptions, model selection and validation, and advanced regression techniques. Ethical considerations in machine learning and AI are also addressed, highlighting the importance of responsible and ethical practices in the digital business environment. With its comprehensive coverage and practical exercises, "Understanding the Fundamentals of Machine Learning and AI for Digital Business" is an essential resource for students, professionals, and anyone interested in harnessing the power of machine learning and AI in the digital era. It offers a solid foundation in theory and practical applications, equipping readers with the skills to navigate the evolving landscape of machine learning and AI and drive digital business success.

Foundations Of Machine Learning Second Edition

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Author : Mehryar Mohri
language : en
Publisher: MIT Press
Release Date : 2018-12-25

Foundations Of Machine Learning Second Edition written by Mehryar Mohri and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-25 with Computers categories.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Introduction To Machine Learning

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Author : Ethem Alpaydin
language : en
Publisher: MIT Press
Release Date : 2014-08-22

Introduction To Machine Learning 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 2014-08-22 with Computers categories.

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.