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Entering the machine learning field, there is an overwhelming wealth of resources. Not all resources are created equally, and many resources may not be optimal for the learning process.
Explore the top 10 free data science books to know in 2025 to help you with your learning and machine learning journey.
1. Machine Learning Fundamentals
Before you can proceed to implement complex machine learning, you need a strong foundation. Machine Learning Fundamentals by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar learn the basic theory of many machine learning techniques used in a variety of applications, covering topics such as:
- PAC learning framework and generalization theory
- Supports vector machines and kernel methods
- Boost and Online Learning Algorithms
- Multi-class classification, ranking, and regression
- Maximum Entropy Model and Reinforcement Learning
If you want to understand more about how machine learning works, start with this book.
2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Although there are already basic theories, we need to learn how to apply machine learning models 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, learns from theory and its applications, including:
- Machine Learning Fundamentals
- Mathematics for Machine Learning
- Data preparation
- Machine Learning Operations
- Responsible and Explainable AI
If you need practical resources to teach your application, don't miss this book.
3. Mathematics for Machine Learning
Many machine learning algorithms consist of mathematical and statistical equations that can be learned from data. Therefore, understanding the mathematics behind machine learning is advantageous. Marc Peter Deisenroth, A. Book Mathematics for Machine Learning by AldoFaisal and Cheng Soon Soon Ong covers a variety of topics, including:
- Linear algebra
- Vector calculation
- Probability and distribution
- Continuous optimization
- Machine learning problems
If you are serious about implementing machine learning, you should read this book.
4. Algorithms for decision making
Machine learning algorithms can help you in your business to make better decisions. You can delegate to the machine to rely on data patterns to decide something. Using algorithms for decision making by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, you will learn how to understand decision making algorithms by learning the following topics:
- Probabilistic reasoning
- Sequential decision making
- Confessional Nation Plans and State Estimates
- Multi-agent decision making
- Practical implementation
This book will help you better understand why machine learning models can help you make decisions.
5. Learn to quantify
All previous books have given us the foundations of machine learning. Now, it's time to learn something more specific. Learning quantification by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, and Fabrizio Sebastiani will learn more about quantification, a monitored learning task for estimating the prevalence of classes of invalid data. There are many topics this covers, including:
- Basics of quantification
- Quantification experiments and evaluation
- Monitored learning for quantification
- Practical applications of quantification
- The evolution of quantification research
This is one of the most exciting fields available, so you'll dive into the quantification field in this book.
6. Gradient Expectations: Structure, Origin, and Synthesis of Predictive Neural Networks
Neural networks have become the standard for many modern machine learning models. By investigating the neural structure of the mammalian brain, we can learn how artificial networks can function as predictive models. Gradient Expectations: The structure, origin and synthesis of predictive neural networks by Keith L. covers a variety of topics, including:
- Prediction concept foundation
- Biological concepts for prediction
- Emergence of predictive networks
- Evolving artificial prediction network
Read this book to understand the basic concepts of advanced machine learning models.
7. Reinforcement Learning: Introduction
Reinforcement learning is the basis of self-monitoring learning in which models understand what happens in the environment and respond based on those events. Reinforcement Learning: An introduction by Richard S. Sutton and Andrew G. Barth combine the basic principles of reinforcement learning that are learned through these topics with real-world applications.
- Fundamentals of Reinforcement Learning
- RL Core Algorithm
- Policy Gradients and Actors – Critical Methods
- Function approximation technique
- Off-Policy Learning
- RL Applications
If you're even more interested in reinforcement learning, don't miss this book.
8. Interpretable machine learning
Books:
Machine learning helps you create predictions and make decisions based on data. However, algorithms often do not explain why they predict a particular value in a particular way. Understanding predictions is important for end users as they are essential to building trust in results. Interpretable machine learning provides a way for users to understand machine learning topics, while interpretable machine learning by Christoph Molnar teaches about interpretable machine learning through these topics.
- Interpretability goals
- Interpretable models
- How local model exists
- Global Models and Protest Methods
- Neural Network Interpretation
If you are a machine learning company, don't miss this book, especially to build trust with users.
9. Fairness and machine learning
Machine learning models are simply tools for learning from historical data. If data biased or unethical is used for model training, it will also be reflected in the prediction or model output. The concept of fairness becomes important in machine learning to ensure that users do not suffer as a result of the model. Explore machine learning fairness through a variety of topics with fairness and machine learning by Solon Barocas, Moritz Hardt and Arvind Narayanan.
- Justification of automatic decisions
- The relative concept of fairness
- Causality
- Understanding the Anti-Discrimination Act
- Testing of discrimination
As a data expert, this book is an important resource for ensuring that our machine learning is ethical and fair.
10. Machine learning in production: From models to products
The best machine learning models are what makes them into production. It doesn't matter how well the model performed. If not used, it is useless. As machine learning practitioners, it has become our job to understand how to move models from the experimental stage to production. Machine Learning in Production: From models to products by Christian Kestner, it teaches you everything you need to know about production.
- Model Systems and Architecture Design
- quality assurance
- Responsible Machine Learning
- Process and Team
Use this book to learn how to deploy models to the best standards.
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 go a few steps ahead of others, improve yourself, and achieve the task you want.
In this article, we explore 10 different free machine learning journals.
Cornelius Judas Ujaya Data Science Assistant Manager and Data Writer. While working full-time at Allianz Indonesia, he loves to share data tips with Python via social and writing media. Cornellius writes about a variety of AI and machine learning topics.
