10 Underrated Books for Mastering Machine Learning
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If you’ve been into machine learning for a while, you’ve probably noticed that the same books get recommended over and over again. Géron’s Hands-On Machine Learning, Goodfellow’s Deep Learning are solid books, no doubt. But there’s a whole world of lesser-known books that go way beyond surface-level explanations. These are the kind of books that don’t just tell you what to do but also why things work the way they do.
So, here’s a list of ten underrated books that deserve more attention. Some are heavy on math, some focus more on concepts, and a few are highly practical. But they all have one thing in common: they’ll make you a better machine learning practitioner.
1. Pattern Recognition and Machine Learning by Christopher M. Bishop
This book is paid and you can get it here, it isn’t for light reading. It’s heavy on Bayesian methods and graphical models—things that a lot of mainstream machine learning books either oversimplify or skip entirely. If you really want to understand why an algorithm makes certain predictions rather than just getting it to work, Bishop’s book is gold.
The best way to use it is to have a solid probability background. To make concepts click, try implementing them in Python with PyMC3 or TensorFlow Probability.
2. The Elements of Statistical Learning by Hastie, Tibshirani, & Friedman
This free book can be gotten here. Think of this as the bridge between classical statistics and modern machine learning. Regression, classification, support vector machines, boosting—it’s all there, and it’s mathematically rigorous. The downside? It’s dense. But if you’ve ever felt like you’re just running models without really understanding them, this book will change that.
Pro tip: Don’t read it straight through—use it as a reference. The PDF is freely available online.
3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
Most machine learning books focus on heuristics. This one? It looks at everything through a probabilistic lens. That means you’ll get a much better grasp of uncertainty in models. Also, Murphy gives Bayesian networks and Monte Carlo methods the depth they deserve.
How to approach it: You can get it here, although it is paid, you can work through the exercises in Jupyter notebooks. Murphy’s explanations are solid, but coding the concepts will make them stick.
4. Bayesian Reasoning and Machine Learning by David Barber
If Bayesian inference has ever felt confusing, this book might just be what you need. It builds an intuitive understanding of Bayesian methods and how they apply to real-world machine learning problems. It also covers Markov Chain Monte Carlo (MCMC) methods in a way that actually makes sense. You can get it here.
A common mistake is ignoring priors. The book does a great job of explaining why priors matter and how to choose them wisely.
5. Learning from Data by Yaser S. Abu-Mostafa
Most books tell you how to apply machine learning, but this one focuses on why it works. Abu-Mostafa explains concepts like the bias-variance tradeoff and learning theory in a way that sticks. Plus, his online course based on the book, is one of the best free machine learning courses out there and you can fid it here.
The best approach is to watch the lectures alongside reading the book. Concepts will sink in much faster.
6. Information Theory, Inference, and Learning Algorithms by David MacKay
Machine learning and information theory go hand in hand, but most machine learning practitioners never actually study the connection. MacKay’s book covers topics like compression, error-correcting codes, and neural networks from an information-theoretic perspective. It’s dense but incredibly insightful.
The best way to use it is to implement the exercises in Python. Writing code forces you to really understand the material.
7. Understanding Machine Learning by Shai Shalev-Shwartz & Shai Ben-David
A lot of machine learning books either dumb things down or bury you in math. This one finds a middle ground. It’s structured well and methodically builds your understanding of both theoretical and applied machine learning, you can get it here.
Who it’s for: If you have some machine learning experience but feel like your theoretical foundation is shaky, this book will help.
8. Mathematics for Machine Learning by Marc Peter Deisenroth, et al.
A lot of people struggle with machine learning because they never properly learned the math behind it. This book fixes that, and it is available for free. It covers linear algebra, probability, and optimization—the core mathematical tools for machine learning.
The best way to learn is to implement the concepts in NumPy and TensorFlow as you go. Math makes way more sense when you see it in action.
9. Neural Networks and Deep Learning by Michael Nielsen
If you want an intuitive understanding of deep learning—without drowning in equations—this is the book. It uses a narrative style to explain core concepts like backpropagation and gradient descent. Even if you already know deep learning, Nielsen’s explanations might give you a fresh perspective.
Bonus: The book is freely available online.
10. Machine Learning for Hackers by Drew Conway & John Myles White
Most machine learning books are either too theoretical or too hands-on. This one finds a balance. It walks through real-world case studies, showing how machine learning can be applied to actual problems rather than just textbook datasets, you can get it here.
The best way to use it is to modify the example code to explore different approaches. The more you experiment, the better you’ll learn.
Wrapping Up
There’s no single book that will teach you everything about machine learning. But reading the right books at the right time can make a huge difference. The ones on this list will challenge you, fill in knowledge gaps, and give you a deeper understanding of machine learning than most mainstream books ever will.
So, which of these books have you read? Are there any hidden gems you’d recommend? Let’s talk about it.
References
- Christopher M. Bishop, Pattern Recognition and Machine Learning – Springer
- Hastie, Tibshirani, & Friedman, The Elements of Statistical Learning – Stanford
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective – MIT Press
- David Barber, Bayesian Reasoning and Machine Learning – UCL
- Michael Nielsen, Neural Networks and Deep Learning – Online
- Aurélien Géron, Hands-On Machine Learning – O’Reilly
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning – MIT Press
- Official Scikit-learn Documentation – Scikit-learn.org
- Official TensorFlow Documentation – TensorFlow.org
