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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →The best AI books to read for free online are Dive into Deep Learning for coding, Mathematics for Machine Learning for prerequisites, Understanding Deep Learning for modern concepts, Deep Learning for rigorous theory, and Probabilistic Machine Learning for a broad foundation. Your best starting point depends on your math and Python experience.
These are not an objective ranking of the five universally best books. They are a progression: practical implementation first, mathematical support where needed, modern architectures next, then deeper theory and probability.
Key takeaways
- Dive into Deep Learning is the strongest first choice for a beginner with basic Python who wants to learn by writing and running code.
- Mathematics for Machine Learning is the best bridge when linear algebra, calculus, probability, or optimization makes AI books difficult to follow.
- Understanding Deep Learning offers a modern, visually clear route through fundamentals, transformers, and diffusion models.
- Deep Learning is a rigorous, comprehensive reference rather than the easiest first book.
- Probabilistic Machine Learning: An Introduction connects classical machine learning and modern deep learning through probability and uncertainty.
- Free online reading does not automatically permit redistribution or commercial reuse, so check the terms on each official book page.
What are the best AI books I can read for free online?
These five legal free AI books form a useful path from foundations to modern deep learning: Dive into Deep Learning for coding, Mathematics for Machine Learning for essential mathematics, Understanding Deep Learning for current concepts, Deep Learning for rigorous theory, and Probabilistic Machine Learning for a broad probability-based framework.
The list is an editorial progression, not an objective universal ranking. The right starting point depends on whether you need Python practice, mathematical preparation, a broad introduction to artificial intelligence, or a deep technical reference.
#1 Best Overall
Which machine-learning book should you read first?
For most readers with basic Python, start with Dive into Deep Learning. The book’s official preface says, “This book teaches deep learning concepts from scratch,” while also assuming modest knowledge of linear algebra, calculus, probability, and Python. The book combines explanations, equations, executable notebooks, and model implementations.
Readers who already have comfortable mathematics can begin with Mathematics for Machine Learning, then move to Probabilistic Machine Learning: An Introduction and use Deep Learning as a reference. That ordering is an editorial recommendation based on the books’ stated scope and prerequisites, not a publisher-prescribed curriculum.
| Book | Best for | Prerequisites | Learning mode | Scope and modern coverage | Free format or access |
|---|---|---|---|---|---|
| Dive into Deep Learning | Beginners who want implementation practice | Basic Python plus modest linear algebra, calculus, and probability | Interactive explanations, code, mathematics, and notebooks | Deep learning, with implementations in PyTorch, NumPy/MXNet, JAX, and TensorFlow | Interactive online book and code-based materials |
| Mathematics for Machine Learning | Readers blocked by equations | Basic mathematical maturity; develops linear algebra, calculus, probability, and optimization | Structured mathematical foundation with tutorials and supporting material | Mathematics needed for machine-learning methods | Free PDF, additional chapters, tutorials, and errata |
| Understanding Deep Learning | Readers seeking a clear modern overview | Some mathematics and programming are helpful | Intuitive explanations balanced with mathematical precision and implementation | Fundamentals, transformers, diffusion models, and newer model families | Free online access associated with the official book resource |
| Deep Learning | Advanced study and technical reference | Basic machine-learning vocabulary and mathematical comfort | Formal, comprehensive, derivation-heavy reference | Broad deep-learning theory and methods | Complete online version available to read free |
| Probabilistic Machine Learning: An Introduction | Readers interested in probability, uncertainty, and synthesis | Comfort with probability and mathematical reasoning is useful | Probability-centered theory with code, figures, and teaching resources | Classical machine learning connected with modern deep learning | Free draft PDF, code, figures, and teaching resources |
For readers who want a printed reference alongside free online reading, an optional buy the print edition can make equation-heavy or code-heavy books easier to annotate. Purchasing a paper copy is not required to access the free online resources, and print availability can differ by title, edition, and country.
1. Dive into Deep Learning: best for learning by implementation
Dive into Deep Learning is the best first choice for a reader who wants the best free book for learning deep learning with Python through practical work. The official project presents the book as an interactive resource combining code, mathematics, notebooks, and discussion rather than a static sequence of definitions.
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The book supports implementations using PyTorch, NumPy/MXNet, JAX, and TensorFlow. That breadth is useful if you want to compare frameworks, but beginners should choose one framework and follow the examples consistently instead of trying to learn every implementation at once.
The book is especially suitable for a complete beginner who already knows basic Python. The official Dive into Deep Learning preface says that no previous deep-learning or machine-learning background is required, while modest linear algebra, calculus, probability, and Python are assumed.
The project reports adoption by 500 universities in 70 countries on its 2026 page capture. That figure is context about the project’s reach, not an independent ranking proving that the book is better than every alternative.
2. Mathematics for Machine Learning: best when the equations are the obstacle
Mathematics for Machine Learning is the best free AI textbook to use before or beside technical machine-learning material when equations are slowing you down. The book focuses on linear algebra, analytic geometry, matrix decompositions, vector calculus, probability, and optimization—the mathematical tools that recur throughout AI.
The official companion site provides a free PDF as well as additional chapters, tutorials, and errata. The material works best as a bridge: read a relevant section when a model, loss function, gradient, or probability expression becomes opaque, then return to the practical book you are studying.
This is not necessarily the most effortless first AI book. Readers who want to build a neural network immediately may find Dive into Deep Learning more motivating, while readers who already understand the mathematics can move directly to a broader machine-learning or deep-learning text.
3. Understanding Deep Learning: best for modern concepts and visual clarity
Understanding Deep Learning is the strongest choice for readers who want a modern conceptual route through deep learning without giving up mathematical precision or practical implementation. MIT Press describes the book as covering transformers and diffusion models alongside the fundamentals.
The book is a useful corrective to older introductions that explain feed-forward networks and convolutional models but give little attention to the architectures now central to contemporary AI discussions. Its modern coverage does not eliminate the need to understand basic optimization, representations, and training; those foundations remain necessary for interpreting newer models.
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Rank #3
4. Deep Learning: best as a rigorous reference
The official Deep Learning site by Ian Goodfellow, Yoshua Bengio, and Aaron Courville makes the complete online version available to read free. The authors state, “The online version of the book is now complete and will remain available online for free.”
This book is a foundational reference for readers who already recognize basic machine-learning terminology and can work through substantial mathematics. It explains the mathematical and conceptual machinery behind deep learning in much greater depth than a lightweight beginner tutorial.
That rigor is also the main limitation for a true beginner. If you are still learning what training, loss, backpropagation, or overfitting mean, begin with Dive into Deep Learning or use Mathematics for Machine Learning as preparation. Return to Deep Learning when you want formal explanations, derivations, and a durable reference.
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5. Probabilistic Machine Learning: An Introduction: best for uncertainty and breadth
Probabilistic Machine Learning: An Introduction is the best choice for readers who want probability and uncertainty to organize their understanding of machine learning. Kevin Murphy’s official page identifies the book as a 2022 MIT Press title and provides a free draft PDF, code, figures, and teaching resources.
The book connects classical machine learning with modern deep learning rather than treating neural networks as the whole of AI. That makes it valuable for readers who want to understand statistical modeling, inference, prediction, and uncertainty across several families of methods.
It is better suited to deliberate study than casual browsing. Probability-centered explanations can be demanding, but the broad framework helps explain why a model makes uncertain predictions and how machine-learning methods relate to one another.
Rank #4
Do you need math before learning AI?
You do not need advanced mathematics before opening an AI book, but you will need some linear algebra, calculus, probability, and optimization to understand technical material fully. A practical reader can start with Dive into Deep Learning and consult Mathematics for Machine Learning whenever an equation becomes a barrier.
The best route depends on the specific barrier:
- You cannot follow vectors, matrices, or matrix multiplication: begin with the linear-algebra sections of Mathematics for Machine Learning.
- You understand the ideas but not gradients or optimization: study the calculus and optimization material in Mathematics for Machine Learning.
- You can code but lack model intuition: start with Dive into Deep Learning and run the notebooks.
- You want a probability-based account of prediction and uncertainty: choose Probabilistic Machine Learning: An Introduction.
- You want formal deep-learning derivations: use Deep Learning after building vocabulary and mathematical comfort.
What is the best reading order for these free AI books?
For a reader with basic Python, the most approachable progression is Dive into Deep Learning, Mathematics for Machine Learning as a companion, Understanding Deep Learning, Deep Learning, and finally Probabilistic Machine Learning: An Introduction.
- Start with Dive into Deep Learning to gain intuition by implementing models.
- Keep Mathematics for Machine Learning open and repair mathematical gaps as they appear.
- Read Understanding Deep Learning to connect foundational ideas with transformers and diffusion models.
- Use Deep Learning for deeper theory, derivations, and reference-quality explanations.
- Finish with Probabilistic Machine Learning: An Introduction for a broad probability-based synthesis.
For a mathematically comfortable reader, reverse the emphasis: start with Mathematics for Machine Learning, move to Probabilistic Machine Learning, and consult Deep Learning for deep-learning-specific theory. The two routes are recommendations, not fixed requirements.
What if you want broader artificial intelligence than deep learning?
Artificial Intelligence: Foundations of Computational Agents is the useful alternative for readers interested in search, logic, agents, planning, and the wider science of AI. The official University of British Columbia page identifies a 2023 third edition from Cambridge University Press and provides the full text online.
This book is a better fit than the five machine-learning-heavy recommendations if your interests include symbolic reasoning, computational agents, decision-making, and planning. The UBC page says that the online version is free to view and download for personal use. Personal-use access should not be interpreted as permission to redistribute the files or use them commercially.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAre these legal free AI textbooks?
Yes, the listed books are available through official author, university, publisher, or open-source project pages rather than unauthorized copies. Free access still has conditions: one book may offer an online HTML edition, another a draft PDF, and another a download limited to personal use.
Best Value
Use the official source pages linked in this article, and check the current copyright or reuse terms before copying, redistributing, modifying, or commercially republishing any book or its figures. Free reading is not the same as unrestricted licensing.
Frequently Asked Questions
What is the best AI book for beginners?
The best free AI book for a beginner with basic Python is Dive into Deep Learning because it teaches concepts through interactive code, mathematics, notebooks, and discussions. The book assumes modest linear algebra, calculus, probability, and Python, but no previous machine-learning or deep-learning background.
Do I need math before learning AI?
You can learn introductory AI without advanced mathematics, but technical progress eventually requires linear algebra, calculus, probability, and optimization. Start practically with Dive into Deep Learning and use Mathematics for Machine Learning to fill gaps as they arise.
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Is Dive into Deep Learning really free?
Dive into Deep Learning is really free to read through its official open-source online project, which includes interactive material and implementations. Free access does not automatically grant permission to redistribute or commercially reuse every book, figure, or file.
Where can I read Deep Learning by Goodfellow for free?
You can read Deep Learning by Goodfellow, Bengio, and Courville for free on the authors’ official website. The site states that the complete online version is available to read free, while readers should still follow the site’s copyright and reuse terms.
The Bottom Line
For most beginners with basic Python, start with Dive into Deep Learning. Add Mathematics for Machine Learning when equations become difficult, read Understanding Deep Learning for transformers and diffusion models, use Deep Learning as a rigorous reference, and choose Probabilistic Machine Learning for a probability-centered view of machine learning. Readers seeking search, logic, planning, or agents should choose Artificial Intelligence: Foundations of Computational Agents instead.
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