These days, artificial intelligence is more than just a catchphrase; it’s a fundamental ability in business, technology, design, healthcare, and almost every other sector you can think of. But with courses costing hundreds or even thousands of dollars, studying AI may frequently feel daunting.
The good news is that 2025 has seen the release of a large number of excellent, free AI books that are on par with even the priciest courses. These books, which have been carefully selected from universities, academics, and professionals, are more than just PDFs; they are organized, enlightening, and ideal for anyone who is serious about learning AI without breaking the bank.
These ten books are revolutionary, regardless of whether the reader is a novice or seeking to expand on prior knowledge.
Top 10 Best AI Books
1. Demystifying Artificial Intelligence by Emmanuel Gillain
Best For: Those who would like to still build their website but think that it is something that requires a computer science degree.
What Makes It Special: Beautifully written, a guide to a world people are talking about without your having to understand a lot of technical jargon. From symbolic AI to neural networks, it’s a historical and philosophical look at modern AI.
Why Read: It makes complicated subjects accessible; great for marketers, teachers, or the plain curious.
2. Artificial Intelligence: Foundations of Computational Agents by David L. Poole and Alan Mackworth
Best For: Students and academic learners
What Makes It Special: A university-level textbook used in top institutions. Focuses on agent-based AI, decision theory, logic, and planning.
Why Read: The structured approach makes it suitable for long-term learning or as a course companion.
3. Neural Networks and Deep Learning by Michael Nielsen
Best For: Self-learners and coders
What Makes It Special: Offers a hands-on, visual explanation of how neural networks work, with Python examples.
Why Read: Ideal for learners who want to build their own neural network from scratch.
4. Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Best For: Advanced learners and AI professionals
What Makes It Special: Often referred to as the “AI Bible,” this book dives deep into mathematical foundations and cutting-edge research.
Why Read: Written by pioneers in deep learning, it’s a must-read for serious AI enthusiasts.
As you work through these AI books and begin to explore more complex topics, having the right tools can make a big difference. In fact, platforms like You.com can support your learning by helping you search smarter, summarize dense material, and stay organized, all while you study.
5. The Hundred-Page Machine Learning Book by Andriy Burkov
Best For: Busy professionals who need clarity fast
What Makes It Special: Condenses years of ML knowledge into 100 pages without losing depth. Covers supervised learning, unsupervised learning, and key algorithms.
Why Read: Quick to consume but deeply informative. A great refresher or starting point.
Free Preview Version Available
6. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Best For: Intermediate and Advanced Students
What Makes It Special: A comprehensive resource on the reinforcement learning problem, branching out with real-life examples including games and robotics.
Why Read: A classic and often used in university programs.
7. Dive into Deep Learning (D2L.ai)
Best For: If you like to learn by doing, then Codecademy allows you to get almost immediate hands-on experience while being walked through the practical aspects of coding by an instructor.
What Makes It Special: Theory as well as applied in live Jupyter Notebook demos using MXNet, PyTorch, and TensorFlow.
Why Read: As interactive as it gets. Also, there are code labs and exercises following each concept.
8. Probabilistic Machine Learning by Kevin P. Murphy
Best For: Learners interested in Bayesian methods and probability
What Makes It Special: Offers a rigorous take on uncertainty in machine learning. Ideal for researchers and data scientists.
Why Read: Adds depth to models by focusing on real-world ambiguity and statistical reasoning.
9. Machine Learning Yearning by Andrew Ng
Best For: Startup founders, product managers, and team leads
What Makes It Special: Focuses on how to design AI systems, rather than just understanding models.
Why Read: Also offers strategic thinking for deploying AI in real-world projects.
10. Data Science for Beginners by Microsoft
Best For: Complete beginners to data + AI
What Makes It Special: Covers the basics of data cleaning, visualization, and predictive analytics through a beginner-friendly lens.
Why Read: Also acts as a soft introduction before diving deep into AI.
As you dive deeper into AI, it’s also worth seeing how it’s used in real-world tools like AI for customer feedback to improve business decisions.
Real-Life Insight: Why These Free Books Outperform Paid Courses
Some readers seem to have learned all the fundamentals of AI from only these books along with free coding platforms such as Google Colab and Kaggle. For example, aspiring developers have cloned real-world projects such as handwritten digit recognition or spam filtering using insights from Nielsen’s and Sutton’s texts.
Lastly, that’s even true in what may not seem like obvious AI-related jobs: Between 2020 and 2021, demand for AI skills has increased 3.9 times for operations roles, 2.6 times for management, three times for sales, four times for design, and 2.2 times for marketing, according to a report by McKinsey.
Which Book to Start With? A Quick Guide
- Absolute beginners: Demystifying Artificial Intelligence, Data Science for Beginners
- Hands-on learners: Dive into Deep Learning, Neural Networks, and also Deep Learning
- Theory buffs: Deep Learning Book, Reinforcement Learning: An Introduction
- Product Thinkers: Machine Learning Yearning
Want more AI reading picks tailored for job prep? Check out our guide on Books to Gain AI Knowledge for Interviews, perfect for acing technical interviews and showcasing your expertise.
You Don’t Need a Degree or a Dollar to Learn AI in 2025
All things considered, diving into artificial intelligence no longer requires an elite degree or a pricey subscription. In fact, with these 10 thoughtfully selected free books, anyone, whether a student, a career switcher, or a curious professional, can begin learning AI right now.
Moreover, each book brings a unique perspective, whether it’s hands-on coding, strategic thinking, or theoretical depth. From foundational texts to advanced neural networks, this list ensures there’s something for everyone. Even better, most of these resources are used in top universities or by working professionals, which means readers are learning from the best.
In the end, the only things needed are curiosity and a willingness to learn. And who knows? One of these books might just be the spark that leads to your next big idea, career breakthrough, or startup success.
So, instead of waiting for the “perfect” course or costly program, start here. Start now. Start free.
FAQs
Yes, all the books listed in this article are 100% free to access or download. Some are hosted on university websites or author pages, while others are open-source projects. A few might ask for an optional email sign-up, but none require payment or subscription.
Reading these books provides a solid theoretical foundation, but pairing them with hands-on coding is essential for real learning. Books like Dive into Deep Learning and Neural Networks and Deep Learning come with interactive code and exercises that help you apply what you’ve learned in real-time.
If you’re brand new to artificial intelligence and, moreover, don’t have a technical background, then starting with Demystifying Artificial Intelligence by Emmanuel Gillain or Data Science for Beginners by Microsoft is a smart move. Both are clearly written, beginner-friendly, and gradually build your foundational understanding step by step.
Most of these books have been updated or are evergreen in their content. For example, The Deep Learning Book by Ian Goodfellow and Machine Learning Yearning by Andrew Ng cover principles and strategies that remain relevant regardless of technological shifts. The tools may change, but the core concepts remain essential.
Absolutely. Reinforcement Learning: An Introduction covers decision-making models used in robotics and gaming (including techniques similar to those used in self-driving cars), while Neural Networks and Deep Learning and Dive into Deep Learning teach the core architectures behind models like ChatGPT.
