10 Free Machine Learning Books For 2025

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Entering the machine learning field presents an overwhelming abundance of resources, sometimes even too many. Not every resource is created equal, and many resources might not be perfect for your learning process.

To assist your journey of learning and mastering machine learning learning, let’s explore the top ten free data science books you should know about in 2025.

1. Foundations of Machine Learning

 
Before moving on to any complex machine learning implementation, we need to have a strong foundation. In Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, you will learn the fundamental theories of many machine learning techniques used in various applications, covering topics such as:

• The PAC Learning Framework and generalization theory
• Support Vector Machines and Kernel Methods
• Boosting and Online Learning Algorithms
• Multi-class classification, Ranking, and Regression
• Maximum Entropy Models and Reinforcement Learning

Start with this book if you want to understand the details of how machine learning works.

2. Practical Machine Learning: A Beginner’s Guide with Ethical Insights

 
While we already have the foundational theory, we still need to learn how to apply the machine learning model with ethical considerations. In the book Practical Machine Learning: A Beginner’s Guide with Ethical Insights by Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja and Kulwa Malyango, you will learn from the theory and its applications, which include:

• Machine Learning Fundamentals
• Math for Machine Learning
• Data Preparations
• Machine Learning Operations
• Responsible AI and Explainable AI

If you need a practical resource that teaches you the application, you should not miss this book.

3. Mathematics for Machine Learning

 
Many machine learning algorithms consist of mathematical and statistical equations that can learn from our data. Therefore, understanding the math behind machine learning is advantageous. In the book Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, various topics will be covered, including:

• Linear Algebra
• Vector Calculus
• Probability and Distribution
• Continous Optimization
• Machine Learning Problems

If you are serious about Machine Learning implementation, then you need to read this book.

4. Algorithms for Decision Making

 
Machine learning algorithms are useful in business to make better decisions. As we rely on the data pattern to decide something, we can delegate it to the machine. With Algorithms for Decision Making by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, you will learn how to understand the decision-making algorithm by learning the following topics:

• Probabilistic Reasoning
• Sequential Decision-Making
• Belief-State Planning and State Estimation
• Multi-Agent Decision Making
• Practical Implementation

This book will help you better understand why the machine learning model is useful for decision-making.

5. Learning to Quantify

 
All the previous books have given us the foundation for machine learning; now, it’s time to learn something more specific. Learning to Quantify by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, and Fabrizio Sebastiani, we will learn more about quantification, which is the supervised learning task to estimate class prevalence in unlabeled data. There are many topics that this will cover, including:

• Fundamentals of Quantification
• Quantification Experimental and Evaluation
• Supervised Learning for Quantification
• Real-World Application of Quantification
• Evolution of Quantification research

Dive into the quantification field with this book, as it’s one of the most exciting fields available.

6. Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks

 
Neural Networks have become the standard of many modern machine learning models. By exploring the neural architecture of the mammal brain, the artificial networks are able to learn how to act as a predictive model. Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks by Keith L. Downing covers various topics, including:

• Prediction Concept Foundation
• Biological Concept for Prediction
• Emergence of Predictive Networks
• Evolving Artificial Predictive Networks

Make sure you read this book to understand the foundational concept of the advanced machine learning model.

7.Reinforcement Learning: An Introduction

 
Reinforcement learning has become a foundation for self-supervised learning, where the model understands what happens in the environment and reacts based on those happenings. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto will combine foundational principles and real-world applications of reinforcement learning, which you will learn through these topics:

• Foundations of Reinforcement Learning
• RL Core Algorithm
• Policy Gradient and Actor-Critic Methods
• Function Approximation Techniques
• Off-Policy Learning
• RL Applications

Don’t miss this book if you are interested further in Reinforcement Learning.

8. Interpretable Machine Learning

 
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Machine learning is useful for creating predictions and making decisions based on data. However, the algorithm often does not explain why it predicts certain values in particular ways. For end users, understanding the prediction is important, as it’s essential for building trust in the results. Interpretable machine learning offers a way for users to comprehend the machine learning topic, and Interpretable Machine Learning by Christoph Molnar will teach you about interpretable machine learning through these topics:

• Goals of Interpretability
• Interpretable Models
• Local Model-Agnostic Method
• Global Model-Agnostic Method
• Neural Network Interpretation

Don’t miss this book if you are working with machine learning, especially to build trust with users.

9. Fairness and Machine Learning

 
A machine learning model is simply a tool that learns from historical data. If any biased or unethical data is used for model training, it will also be reflected in the predictions or the model output. Concepts of fairness become crucial in machine learning to ensure that users do not suffer harm as a result of the model. In Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan, we will explore fairness in machine learning through various topics:

• Legitimation of Automated Decision
• Relative Notion of Fairness
• Causality
• Understanding Anti-Discrimination Law
• Testing Discrimination

As a data professional, this book is an important resource to ensure that our machine learning works is ethical and fair.

10. Machine Learning in Production: From Models to Products

 
The best machine learning model is the one that makes it into production. It doesn’t matter how good your model’s performance was; if it wasn’t used, then it is useless. As machine learning practitioners, it has become our job to understand how to move the model from the experiment phase into production. Machine Learning in Production: From Models to Products by Christian Kästner will teach you everything you need to know about production, with topics including:

• Model System and Architecture Design
• Quality Assurance
• Responsible Machine Learning
• Process and Teams

Learn how to deploy your model with the best standard using this book.
 

Conclusion

 
Machine learning is an exciting tool to learn, but it’s not easy to understand everything that machine learning has to offer. With these resources, you can stay a few steps ahead of others to improve yourself and achieve the job you want.

In this article, we will explore ten different free machine learning books.
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.