Interested in machine learning? Here are the best machine learning books to expand your knowledge in this popular subset of artificial intelligence.
Machine learning algorithms power the world around us, affecting various aspects of our everyday lives, from product recommendations on our grocery apps to fraud detection systems at our local banking services.
Below is a list of the best machine learning books that provide a clear explanation of how exactly machine learning works.
Whether you’re a beginner or you already have some knowledge in this area, these machine learning books will increase your understanding of important topics including neural networks, deep learning, advanced machine learning methods, and model evaluation.
These books will also give you the opportunity to dive deeper into case studies and examples of the numerous practical applications of machine learning, including the little details that often get overlooked.
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Here are the best machine learning books to add to your reading list.
In this bestselling machine learning book, artificial intelligence expert Andriy Burkov has undertaken the very useful yet challenging task of reducing all of machine learning to 100 pages.
This book combines a wide range of machine learning topics in a systematic, easy-to-read format. It features only those parts of the huge body of material on machine learning developed since the 1960s that have proven to have a significant practical value.
A beginner in machine learning will find in this book just enough details to get a comfortable level of understanding of the field and start asking the right questions.
On the other hand, those with prior experience in machine learning can use this book as a collection of pointers to stimulate further self-improvement.
This book also comes in handy when brainstorming at the beginning of a project, when figuring out whether a given technical or business problem is "machine-learnable" and, if yes, which techniques you should try to solve it.
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Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us; manage our investments; and discover new drugs.
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.
In the Master Algorithm, Pedro Domingos, one of the field's leading experts, lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone.
In the book, Domingos assembles a blueprint for the future universal learner algorithm, discussing what it will mean for business, science, and society.
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This popular book about machine learning is one of Elon Musk's personal favourites.
Deep Learning offers a mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.
It describes deep learning techniques used by practitioners in the industry, and surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
This book is one of the best machine learning books for undergraduate or graduate students planning careers in either industry or research, and it's also quite useful for software engineers who want to begin using deep learning in their products or platforms.
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If you're curious about the role that data science and machine learning play in modern marketing strategies, this is the book for you.
In this book, you'll learn to implement data science techniques to identify the factors behind the successes and failures of marketing campaigns.
More specifically, you'll focus on how to use machine learning techniques to understand different customer segments, predict which customers are more likely to engage with your products, and recommend the right products for each customer.
You'll also learn how to:
By the end of this book, you will be well-versed with a variety of data science and machine learning techniques to run and manage successful marketing campaigns for your business.
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This machine learning book has been written and designed for absolute beginners.
As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning.
Where core algorithms are introduced, logical explanations and visual examples are added to make it easy and engaging to follow along.
In this book, you will learn:
Please note: This book is designed for readers taking their first steps in machine learning. If you have passed the "beginner" stage in your study of machine learning, you would be better served with a long-format textbook instead of this book.
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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning.
This practical book teaches programmers of all levels and experience how to use simple, efficient tools to implement programs capable of learning from data.
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.
As you read this book, you will:
This book covers a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
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Pattern Recognition and Machine Learning by Christopher M. Bishop is the first textbook on pattern recognition to present the Bayesian viewpoint.
It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.
While this isn't a book for beginners, it's an information-rich read aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners.
Familiarity with multivariate calculus and basic linear algebra is required.
Also, some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
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Machine Learning: An Applied Mathematics Introduction is a fully self-contained guide that covers the essential mathematics behind all of the most important machine learning techniques.
This book is a launch pad for anyone who wants to understand the foundations and put the tools into practice.
All that the reader requires is an understanding of the basics of matrix algebra and calculus.
The book includes many real-world examples from a variety of fields including finance (volatility modelling), economics (interest rates, inflation and GDP), sociology (classifying locations according to crime statistics), and many more.
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The Elements of Statistical Learning describes important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.
It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning.
The many topics covered in the book include neural networks, support vector machines, classification trees and boosting. While the approach taken is statistical, the emphasis is on concepts rather than mathematics.
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Learning from Data is designed to be a short, well-paced course on machine learning.
The book provides a complete overview on the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data.
It also provides a detailed explanation on why and when statistical learning methods work, while introducing the mathematical prerequisites required to go out and further explore the field.
The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU). They have evidently consulted extensively with financial and commercial companies on machine learning applications.
Many readers have found this book useful in combination with Professor Yaser's YouTube lecture series on the topic.
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Thanks for reading this article on the best machine learning books to read in 2021.
Of course, the particular book(s) you choose to read on this subject will depend on your personal areas of interest. Another deciding factor is your comfort level with this topic, and whether you're a beginner or experienced machine learning practitioner.
As you grow your knowledge of machine learning, you'll be preparing yourself for the rapid digital transformation that we can expect in the years to come.
Happy learning!
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