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.
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.
At Simplilearn, our Machine Learning Certification Training Course teaches students the skills they need to either start their careers or advance in an existing one. The course also prepares students for the certification exam. Students in this program learn everything from mathematical and heuristic aspects to algorithm development and hands-on modeling. The program includes 44 hours of instruction, plus lifetime access to self-paced learning. Students will also take on four different projects throughout their enrollment and get to practice what they have learned with more than 25 hands-on exercises.
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