Machine learning has become more and more integrated into our lives. It is the branch of artificial intelligence (AI) that powers chatbots, customizes the shows that Netflix recommends for you, and determines your TikTok feed. It plays a powerful role in health care technology, where machines can diagnose treatments and even perform surgery.
As businesses begin to understand the value of machine learning, the demand for skilled machine learning engineers and data scientists is also growing. Astoundingly, in 2019, machine learning engineer was ranked the best job in the United States, based on 344 percent job growth between 2015 and 2018 .
If you’re curious about machine learning, books are a wonderful way to immerse yourself in key concepts, terminology, and trends. We’ve curated a list of machine learning books for beginners, from general overviews to those with focus areas, such as statistics, deep learning, and predictive analytics. With these books on your reading list, you’ll be able to:
Determine whether a career in machine learning is right for you
Learn what skills you’ll need as a machine learning engineer or data scientist
Knowledge that can help you find and prepare for job interviews
Stay on top of the latest trends in machine learning and artificial intelligence
Bookmark this page now so you can revisit it throughout your machine learning journey.
There are many excellent books on machine learning and artificial intelligence, but these titles are especially useful for beginners who are just discovering this field. Most of these deliver an overview of machine learning or an introduction through the lens of a specific focus area, such as case studies and algorithms, statistics, or those who already know Python.
Best for machine learning overview
In just (a little over) one hundred pages, this book offers a solid introduction to machine learning. It’s written in a way that makes the artificial intelligence systems very easy to understand so that you would be prepared to speak about basic concepts in an interview. The book combines both theory and practice, illuminating the significant approaches such as classical linear and logistic regression with illustrations, models, and algorithms written with Python.
It’s not for absolute beginners, but is an excellent introduction to the subject and for data professionals looking to expand their machine learning knowledge.
Best for absolute beginners
As the title suggests, this book delivers a basic introduction to machine learning for beginners with zero prior knowledge of coding, math, or statistics. Theobald’s book goes step-by-step, written in plain language, with visuals and explanations accompanying each machine learning algorithm.
If you are entirely new to machine learning and data science in general, this is the book for you.
Best for programmers (who enjoy practical case studies)
Rather than the term “hackers” referring to individuals who gain unauthorized access to people’s data, the authors talk about programmers who hack together code for a specific purpose or project. This book is ideal for those with programming and coding experience but who are less familiar with the mathematics and statistics side of machine learning.
The book uses case studies that offer practical applications of machine learning algorithms, which help to situate mathematical theories in the real world. Examples, such as how to build a recommendation for who to follow on Twitter, keep the abstract concepts grounded.
Did you know?
AI now enables machines to write books, instead of humans. Using the language model Generative Pre-trained Transformer 3 (GPT-3), deep learning produces human-like text.
AI book projects are based on the long short-term memory (LSTM) algorithm, which enables feedback connections and processing of entire data sequences. While the concept can seem creepy, it pushes the boundaries of what’s possible. You can find AI-written books at Booksby.ai.
Best for those who know Python
If you already have experience with Python’s programming language, this book offers further guidance on understanding concepts and tools you’ll need to develop intelligent systems. Hands-On Machine Learning is written so that each chapter features exercises to apply what you’ve learned in previous chapters.
Use this book as a resource for developing project-based technical skills that can help you land a job in machine learning.
Read more: What Is Python Used For? A Beginner’s Guide
Best book on deep learning
This book offers a beginner-friendly introduction for those of you more interested in the deep learning aspect of machine learning. Deep Learning explores key concepts and topics of deep learning, such as linear algebra, probability and information theory, and more.
Best for a statistics approach
This book is an excellent tool for those who already have some knowledge of statistics. You’ll be able to understand statistical learning, and unveil the process of managing and understanding complex data sets. It covers important concepts like linear regression, tree-based models, and resample methods, and includes plenty of tutorials (using R) to apply these methods to machine learning.
Best guide for practical application
As you delve further into machine learning, with this book you’ll learn how to create algorithms for specific projects. It is a practical guide that can teach you how to customize programs that access data from websites and other applications and then collect and use that data. By the end, you’ll be able to create the algorithms that detect patterns in data, such as how to make predictions for product recommendations on social media, match singles on dating profiles, and more.
Read more: 7 Machine Learning Algorithms to Know
Best for an analytics approach
Another book that provides practical applications and case studies alongside the theory behind machine learning. This book is written for those who develop on and with the internet. It takes the guesswork out of predictive data analytics, providing a comprehensive collection of algorithms and models for applying machine learning.
Read more: What is Data Analytics?
Best for a free resource
This final one is an e-book that is free to download . It is a clear, easy-to-read guide for machine learning beginners, accompanied with code, math, and real-world examples for context. In five chapters, you’ll learn why machine learning matters, then become familiar with supervised and unsupervised learning, neural networks and deep learning, and reinforcement learning. As a bonus, it includes a list of resources for further study.
Machine learning in literature
The Book of Why by Judea Pearl and Dana Mackenzie proposes the value of cause and effect in data, and how it can contribute to social good (such as the relationship between carbon emissions and global warming). This notion of causality forms the basis of both human and artificial intelligence.
If fiction is more your speed, Isaac Asimov’s classic I, Robot, imagines how humans and robots would struggle to survive together. Other sci-fi authors like Ted Chiang explore our relationship with AI technology in stories like The Lifecycle of Software Objects.
Machine learning can take you to career paths such as: Machine learning engineer, data scientist, human-centered machine learning designer, computational linguist, software engineer or developer, and business intelligence developer.
If you’re interested in machine learning, the Machine Learning specialization from Stanford University can provide you with job-ready skills to propel your career forward. You’ll gain an understanding of supervised and unsupervised learning, as well as best practices and case studies for a well-rounded introduction to machine learning.
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries ...
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Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Linear Regression, Logistic Regression for Classification
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.