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.
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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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your…
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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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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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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 learning, cryptography and security.
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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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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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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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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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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.
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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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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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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 learning, and causality. This volume puts deep learning into a larger statistical context and unifies …
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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 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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