9 Machine Learning Books for Beginners: A 2025 Reading List

Machine learning has become more and more integrated into our lives. It is the branch of artificial intelligence (AI) that powers chatbots, customises the shows that Netflix recommends for you, and determines your Facebook feed. It plays a powerful role in healthcare 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. According to Statista, the machine learning market in India is projected to grow at a compound annual growth rate of 36.11 per cent until 2030, reaching a value of nearly 18 billion USD [1]. The average yearly salary for a machine learning engineer is ₹10,00,000 [2]. 

Reading books is a wonderful way to immerse yourself in machine learning's key concepts, terminology, and trends. In this curated list of machine learning books for beginners, you’ll find general overviews and 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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Books about machine learning for beginners

Many excellent machine learning and artificial intelligence books are available, 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 for gaining an overview of machine learning

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.

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 with zero prior knowledge of coding, maths, or statistics. Theobald’s book goes step-by-step, using plain language, and contains visuals and explanations alongside each machine-learning algorithm. 

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

Did you know?

AI now enables machines to write books with minimal human input. Deep learning produces human-like text using the language model Generative Pre-trained Transformer 3 (GPT-3).

AI book projects use the long short-term memory (LSTM) algorithm, which enables feedback connections and processing of entire data sequences. Whilst the concept can seem creepy, it pushes the boundaries of what’s possible. You can find AI-written books at Booksby.ai.

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

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. 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 to help you land a job in machine learning.

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: lectures with slides accompanying the book are available on their website with exercises on GitHub.

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. It explains statistical learning and reveals the process of managing and understanding complex data sets. It covers important concepts like linear regression, tree-based models, and resampling methods and includes plenty of tutorials (using R) to apply these methods to machine learning.

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 customise 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 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.

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 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. 

9. Linear Algebra: Step by Step by Kuldeep Singh

Best for learning the mathematics behind machine learning

The final book brings you deeper into the mathematics behind machine learning, namely linear algebra. The basis of linear algebra is linear equations which make up matrices. Since matrices have a similar structure to data in a spreadsheet, understanding linear algebra is extremely useful for ML engineers and data scientists. Understanding linear algebra can help you intuitively understand the basics of machine learning algorithms, even if the majority of them are prebuilt these days.  

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. 

Get started in machine learning with Coursera

Machine learning can take you to career paths such as machine learning engineer, data scientist, human-centred machine learning designer, computational linguist, software engineer or developer, and business intelligence developer.

Consider the Machine Learning Specialisation from Stanford University to gain 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. Other offerings include the Deep Learning Specialisation and the AI for Everyone course from DeepLearning.AI. In these, you'll learn how to build AI projects, train deep neural networks, and more.

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Supervised Machine Learning: Regression and Classification

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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Skills you'll build:

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