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Author: Jeremy Watt
Publisher: Cambridge University Press
ISBN: 1108480721
Category : Computers
Languages : en
Pages : 597
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Book Description
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Author: Jeremy Watt
Publisher: Cambridge University Press
ISBN: 1108480721
Category : Computers
Languages : en
Pages : 597
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Book Description
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
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Book Description
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.
Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262028182
Category : Computers
Languages : en
Pages : 639
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Book Description
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.
Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223
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Book Description
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538
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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author: Jeremy Watt
Publisher:
ISBN: 1108575544
Category : Computers
Languages : en
Pages : 598
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Book Description
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Author: Julian McAuley
Publisher: Cambridge University Press
ISBN: 1009008579
Category : Computers
Languages : en
Pages : 338
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Book Description
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
Author: Willi Richert
Publisher: Packt Publishing Ltd
ISBN: 1782161414
Category : Computers
Languages : en
Pages : 431
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Book Description
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.
Author: Sunila Gollapudi
Publisher: Packt Publishing Ltd
ISBN: 1784394017
Category : Computers
Languages : en
Pages : 468
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Book Description
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
