At MentorCruise, we are all about making the most out of the experience of others. As part of that, we have 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.
Machine Learning for Humans is an introduction to machine learning that is accessible to anyone with a basic understanding of high school mathematics. The book provides a practical understanding of machine learning through a hands-on approach, using Python as the programming language.
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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 …
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Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.
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How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learni…
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Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut throu…
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Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis.
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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.
This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks―scikit-learn and TensorFlow―author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.
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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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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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Python Machine Learning is a comprehensive guide to machine learning, deep learning, and data analysis using Python. In this book, Sebastian Raschka and Vahid Mirjalili provide practical knowledge and techniques for creating and contributing to modern data analysis.
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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 int…
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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 …
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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.
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 le…
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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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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 techniq…
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