Top 8 Best Machine Learning Books to Read

Everyone has their own learning style, and for some, reading can be a great way to brush up on a current skill or learn something new entirely. If you’re interested in the world of machine learning (ML), there are several great books on the topic. Which one is right for you depends on how much you already know.  If you are new to ML, there are some great books designed for beginners. There are also some excellent digital machine learning ebooks available.

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No matter where you are in your machine learning journey, there is bound to be a book that’s right for you. Here are some of our favorites:

1. The Hundred-Page Machine Learning Book

By Andriy Burkov

A favorite from Analytics Vidhya, which describes this book as being beautifully written, this book includes everything you need to know about machine learning. It not only introduces the concept of ML to readers, but dives into the different types of it, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also covers basic practices, advanced practices, classifications, learning algorithm anatomies, and other helpful tips. 

2. Machine Learning: The New AI

 By Ethem Alpaydin

If you are looking for a more recent publication, WhatPixel recommends this book, which is written by a well-known scholar (who is also highly skilled in machine learning). Readers will learn about a variety of techniques and concepts in this book, including pattern recognition, neural networks, deep learning, and learning clusters. 

3. The Elements of Statistical Learning

By Trevor Hastie, Robert Tibshirani, and Jerome Friedman

The Elements of Statistical Learning is reportedly a favorite amongst members of the Hackernoon community. With mathematics and statistics being a core concept of machine learning, this book is a terrific read for those looking to break into the industry. Some of the many topics covered are support vector machines, neural networks, classification trees, and boosting. 

4. Machine Learning in Action

By Peter Harrington

Data visualization and reporting company, Tableau, recommends this book for anyone new to machine learning and that wants to gain a comprehensive understanding of the field. Readers learn the basic fundamentals in this book. It covers dataset splitting, association analysis, the Apriori algorithm, a variety of machine learning tools, and more. 

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5. Machine Learning: A Technical Approach To Machine Learning for Beginners

By Leonard Eddison

Another top pick for beginners and recommended by Solutions Review, this book explains how important ML is today and the technology required to make it work. Not only do readers learn about the basic concepts that underlie ML, but they also learn about advanced concepts, including programming languages (such as Python), logistic regression, decision trees, and much more. 

6. Machine Learning Yearning

By Andrew NG

This book looks at machine learning from a strategic business point of view and focuses on everything from the data science process to data visualization. Readers also learn about some of the key terms they’ll likely hear often in the field. This book is especially helpful for those pursuing a career in the marketing and/or retail industries, by introducing readers to industry-specific ML applications

7. Learning from Data

By Yaser S. Abu-Mostafa

This book offers insight into techniques that apply to specific industries, such as finance, engineering, commerce, and science. If you’re looking for an online option, this is a great choice because readers receive access to online chapters, which are updated regularly to reflect the current trends in the field.

8. Machine Learning For Absolute Beginners: A Plain English Introduction

By Oliver Theobald

Learning about Machine learning can be intimidating when you have no experience in the field. The concepts and terminology can be very complex for a newbie — almost enough to deter some people altogether. This book aims to change that with its simple approach. Visual examples and clear explanations help readers understand the basics of ML. Because this book is geared exclusively to beginners, it is not recommended for those who are in the more advanced learning stages. 

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If you’re looking to build a career in machine learning, it is critical to know as much as possible about this complex field. From the basics to advanced principles, books can be a great way to learn new things, but it may not be practical to rely on books as a primary source. One of the best ways to get formal training in ML is by enrolling in a Machine Learning course that provides comprehensive coverage of the field through a blended learning method. Students who enroll in Simplilearn’s online learning program, students can ask questions and receive feedback from expert instructors, and also practice what they’ve learned through practical hands-on projects.

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