At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite Machine Learning books – and here are the answers.
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Understanding the concepts of Machine Learning starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
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Bishop's book on pattern recognition is a classic textbook and staple in Machine Learning. Beimg aimed at grad students, but also at researchers and practitioners, it's no easy lecture, but a truly fundamental course book.
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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or in…
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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 to practitioners, and for the reader who understands that this is the first 100 (or actual…
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Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
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Richard Sutton's book on Machine Learning is universally regarded as one of the most fundamental and important pieces on the matter. Reinforcement Learning is quickly becoming a major part of AI innovation, and a good read for any engineer and scientists to go through.
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These books are not required for you to learn Machine Learning, but they are highly recommended for you to deepen your knowledge.
Laurence Moroney's “AI and Machine Learning for Coders” is an invaluable guide for programmers looking to transition into the exciting world of AI. This book empowers readers to implement common AI scenarios such as computer vision, NLP, and sequence modeling by taking a practical, code-first approach.
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Key Features
Design, train, and evaluate machine learning algorithms that underpin automated trading strategies
Create a research and strategy development process to apply predictive modeling to trading decisions
Leverage NLP and deep learning to extract tradeable signals from market and alternative data
Book Description
The explosive growth of digital data has boosted the deman…
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Featured by Tableau as the first of ""7 Books About Machine Learning for Beginners."" Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home. R…
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By using concrete examples, minimal theory, and two production-ready Python frameworksâ??Scikit-Learn and TensorFlowâ??author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youâ??ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to…
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“Artificial Intelligence: A Modern Approach” goes beyond the basics and delves into advanced topics in AI. It covers natural language processing, an area concerned with enabling computers to understand and generate human language. The book explores techniques for parsing, semantic analysis, and machine translation.
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This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.
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You've got your basics in order – time to move on to some advanced and specialized concepts. Machine Learning is evolving every day, these books can help you master it.
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 advanced ML algorithms in AWS cloud.
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An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies …
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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 potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that inclu…
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