9 Machine Learning Books for Beginners: a 2025 Guide

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.

As businesses begin to understand the value of machine learning, the demand for skilled machine learning engineers and data scientists is also growing. According to Indeed, in 2023, machine learning engineers were ranked among the top 10 best jobs in the United States.

Reading books is a wonderful way to immerse yourself in machine learning's 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

• Acquire knowledge that can help you find and prepare for job interviews 

• Stay on top of the latest trends in machine learning and artificial intelligence

• Hear from knowledgeable professionals in this field

Bookmark this page now so you can revisit it throughout your machine-learning journey.

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9 machine learning books for beginners

There are many excellent books on machine learning and artificial intelligence, but these titles are especially useful for beginners 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.

1. The Hundred-Page Machine Learning Book by Andriy Burkov

Best machine learning overview

In just over 100 pages, this book offers a solid introduction to machine learning in a writing style that makes AI systems easy to understand. Data professionals can use it to expand their machine-learning knowledge. Reading this book can help you prepare to speak about basic concepts in an interview. The book combines both theory and practice, illuminating significant approaches such as classical linear and logistic regression with illustrations, models, and algorithms written with Python.

Read more: Machine Learning Skills: Your Guide to Getting Started

2. Machine Learning For Absolute Beginners by Oliver Theobald

Best for absolute beginners

As the title suggests, this book delivers a basic introduction to machine learning for beginners who have zero prior knowledge of coding, math, or statistics. Theobald’s book goes step-by-step, is written in plain language, and contains visuals and explanations alongside each machine-learning algorithm. 

If you are entirely new to machine learning and data science, this is the book for you.

3. Machine Learning for Hackers by Drew Conway and John Myles White

Best for programmers (who enjoy practical case studies)

The authors use the term “hackers” to refer to programmers who hack together code for a specific purpose or project rather than individuals who gain unauthorized access to people’s data. 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 Twitter follower recommendations keep the abstract concepts grounded. 

Did you know?

AI now enables machines to write books with minimal human input. Using large language models (LLMs) like ChatGPT, 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.

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4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien

Best for those who know Python

If you already have experience with the Python programming language, this book offers further guidance on understanding concepts and tools you’ll need to develop intelligent systems. Each chapter of Hands-On Machine Learning includes exercises to apply what you’ve learned.

Use this book as a resource for developing project-based technical skills that can help you land a job in machine learning.

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5. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

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. 

Bonus: The book is accompanied by lectures with slides on their website and exercises on Github.

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6. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

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.

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7. Programming Collective Intelligence by Toby Segaran

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

8. Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

Best for an analytics approach

This is 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?

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9. Machine Learning for Humans by Vishal Maini and Samer Sabri

Best for a free resource

This final one is an e-book that is free to download [2]. It is a clear, easy-to-read guide for machine learning beginners, accompanied by 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.

Start advancing your machine learning skills today

Machine learning is responsible for many of the modern AI advances we interact with every day. Learn more about AI with these courses on Coursera:

For a beginner-friendly overview of AI and machine learning, try DeepLearning.AI's AI for Everyone course. Designed for learners without a technical background, this course covers common AI terms, what AI can and can't do, and how to spot opportunities for using it in the workplace.

To develop practical machine learning skills, enroll in Stanford and Deeplearning.AI's Machine Learning Specialization. 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.

To prepare for a career in AI and ML engineering, take the Microsoft AI & ML Engineering Professional Certificate. Here, you'll learn to design and implement AI and ML infrastructure, master ML algorithm techniques, and leverage Microsoft Azure for AI workflows.

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