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.
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts …
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It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data.
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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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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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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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Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.
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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.
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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Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day.
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It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples o
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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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This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.
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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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This list is curated by MentorCruise and can include Amazon affiliate links. Have any other suggestions? Add here.
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