Artificial intelligence has taken the world by storm. As data professionals, it’s become essential to understand AI, its impact, and how to apply it. To help you learn further in your AI journey, this article will outline the top ten free AI books you should know in 2025.
1. Demystifying Artificial Intelligence
Artificial intelligence is often viewed as a buzzword, yet many remain unaware of its full potential. Demystifying Artificial Intelligence by Emmanuel Gillain tries to explore how AI can impact real-world applications and how it can remain relevant in business through the following topics:
- What AI is and how it differs from machine learning
- The differences between symbolic AI and statistical AI
- How AI systems solve problems
- How machines learn from data
- How AI handles uncertainty
- Why ethics and transparency are essential
This book is perfect if you have never explored AI before and want to understand what it could bring to your business.
2. Unlocking Artificial Intelligence
Applying AI to real-world applications is now more important than ever, as this new technology can assist in professional work swiftly. Unlocking Artificial Intelligence by Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann, and Alexander Martin offers a comprehensive resource from fundamental theory to applications of AI, which you could learn from these topics:
- How AI learns from data
- Role of uncertainty quantification in AI systems
- Reinforcement learning agents
- Reinforcement learning agents
- AI application in industrial settings
Be sure to read this book to grasp the complete story of AI in real-world applications.
3. Artificial Intelligence and Evaluation
Previously, assessing programs, policies, research proposals, and many others could take a long time. Evaluation of the documents now becomes easier with the introduction of AI models. In the era of AI emergence, evaluators need to adapt to new developments, and Artificial Intelligence and Evaluation by Steffen Bohni Nielsen, Francesco Mazzeo Rinaldi, and Gustav Jakob Petersson provides evaluators with a way to gain those skills through these topics:
- How AI transforms the evaluation field
- Which digital tools to use
- AI-evaluation risks
- Real-world case studies
Whether you are working in the evaluation field or not, this book will give you new skills that can help you expand your AI career.
4. Artificial Intelligence: Foundations of Computational Agents, 3rd Edition
Agents have become one of the most valuable tools emerging from the AI field, with companies now striving to hire many talented individuals capable of working with agents. As a data professional, learning the foundation of agents has become more important than ever, and this book — Artificial Intelligence: Foundations of Computational Agents, 3rd Edition by David L. Poole and Alan K. Mackworth — will certainly provide you with those skills. In this book, you will learn various aspects of agents, including:
- Agents introduction
- Reasoning and planning with certainty
- Reasoning and planning with uncertainty
- Planning and acting with uncertainty
- The big picture
If you are interested in Agent Implementation, then this book is something you would not want to miss.
5. Neural Networks
Many courses have introduced what a neural network is, but this book delves deeper into investigating its history and its impact on cultural, political, and scientific fields. Neural Networks by Ranjodh Singh Dhaliwal, Théo Lepage-Richer, and Lucy Suchman tries to explain how neural network models are applied in many areas while also describing how they are shaped by history. The book covers the following topics:
- How neural networks are shaped by history
- Analogy of neural networks
- Experimental media in the idea of neural intelligence
- Neural network representation in social
- Why neural networks are important for AI systems today
This book isn’t your typical technical explanation of neural networks; instead, it discusses much more from a societal point of view. It is valuable for any data professional curious about the impact of neural networks.
6. Deep Learning
Deep learning has become the gold standard for any AI models and a skill expected of any data professional who wants to work with AI in depth. In the book Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, you will learn everything you need to know about Deep Learning through these topics:
- Applied math and machine learning basics
- Practical deep networks
- Deep learning research
If you want to enhance your knowledge of Deep Learning, this book is for you.
7. Foundations of Large Language Models
Large language models, or LLMs, have become the inventions that propel the AI name into the public sphere. With LLM-powered products such as ChatGPT, people rely on AI more than ever. Anyone interested in working with natural language generative AI models needs to learn more about LLMs, which Foundations of Large Language Models by Tong Xiao and Jingbo Zhu covers through these topics:
- How LLM are pre-trained
- How generative models are scaled and fine-tuned
- Role of prompting and in-context learning
- LLM techniques for aligning with human preferences
- Key architecture
- Engineering challenge of LLM implementation
Given the importance of LLM in your AI career, this book is something you do not want to miss.
8. Foundation Models for Natural Language Processing
Modern AI systems now rely on pre-trained language models and foundation models. Understanding how they work then becomes important if we want to delve into applying these models in industry, which Foundation Models for Natural Language Processing by Gerhard Paaß and Sven Giesselbach teaches through these topics:
- Design and optimization of pre-trained language models
- Foundation models processing
- Transformer-based architecture
- Foundation model fine-tuning
- Foundation model evaluation
- Ethical and societal implication
Leverage this book the moment you want to become a professional in the AI world.
9. Programming Computer Vision with Python
Image analysis and generation is one of the most exciting areas in AI. The potential is significant, so many data professionals rush to learn about working with images. To support your learning, Programming Computer Vision with Python by Jan Erik Solem will guide you through image analysis by covering the following topics:
- Image handling with Python
- Detecting image features
- Performing image transformation
- Building vision applications
Don’t miss this book if you want to learn optimally for the image data analysis process.
10. Agents in the Long Game of AI: Computational Cognitive Modeling for Trustworthy, Hybrid AI
What is lacking the most in this ear of AI is the trustworthiness of the systems. Many companies are still unsure about using AI, especially agent implementation for executing core business activities. That’s why this book proposes a method called language-endowed intelligent agents (LEIAs), which is a hybrid approach that combines symbolic reasoning with data-driven tools. In Agents in the Long Game of AI: Computational Cognitive Modeling for Trustworthy, Hybrid AI by Marjorie McShane, Sergei Nirenburg, and Jesse English, we will go through the concepts with the following topics:
- Language-endowed intelligent agents (LEIAs)
- Symbolic reasoning and machine learning for trustworthy AI
- Cognitive modelling
- Agent behavior
- Explainability and collaboration in AI
By building a more trustworthy system, you can ensure that your application will be useful for the business.
Conclusion
Living in today’s data world means that we, as data professionals, need to upskill ourselves. There are many resources available to learn AI, but these are ten free artificial intelligence books you should read in 2025.
I hope this has helped!
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.