Top Machine Learning books recommended by experts

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Table of Contents

Fundamentals of Machine Learning

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.

Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction

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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Generative Deep Learning

Generative Deep Learning

Itโ€™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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Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

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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Federated Learning (Synthesis Lectures)

Federated Learning (Synthesis Lectures)

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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Machine Learning For Dummies

Machine Learning For Dummies

Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it.

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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

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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Specializations and Deeper Machine Learning Knowledge

You've got your basics in order โ€“ time to move on to some advanced and specialized concepts. Machine Learning is evolving every day, these books can help you master it.

Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

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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Probabilistic Machine Learning: Advanced Topics

Probabilistic Machine Learning: Advanced Topics

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 approaches based on deep learning with ones based on probabilistic modeling and inference.

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Advanced Machine Learning with Python

Advanced Machine Learning with Python

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 include image, music, text, and financial data.

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