Quick answer: Start with Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow for practical skills, then move to theory. Good's 2017-first-edition framework still holds: one hands-on book, one theory book, and free official documentation. The four books below cover both tracks — three of them are also legally available free online from their authors or publishers.
Which ML book should you read first?
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow first. It teaches through scikit-learn, Keras, and TensorFlow — the libraries you will actually use — and its publisher O'Reilly positions it as a hands-on introduction requiring only basic Python. Pair it with the free scikit-learn user guide at scikit-learn.org, which documents every algorithm the book uses, then add theory once you can train models end to end.
How do the four classic books compare?
| Book | Focus | Prerequisites | Free version |
|---|---|---|---|
| Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow | Practical ML with scikit-learn/Keras/TensorFlow | Basic Python | No (paid) |
| Deep Learning (Adaptive Computation and Machine Learning series) | Deep learning theory (Goodfellow, Bengio, Courville) | Linear algebra, probability | Yes — deeplearningbook.org |
| Pattern Recognition and Machine Learning (Information Science and Statistics) | Bayesian pattern recognition | Calculus, linear algebra | Yes — Microsoft Research PDF |
| The Elements of Statistical Learning: Data Mining, Inference, and Prediction | Statistical learning theory (Hastie, Tibshirani, Friedman) | Statistics background helps | Yes — authors' Stanford page |
Is Deep Learning by Goodfellow et al. still worth reading?
Yes, for foundations. Published by MIT Press in 2016, the book remains the standard reference for feedforward networks, regularization, and optimization; the authors host the full text free at deeplearningbook.org. Its later chapters predate transformers, so pair it with recent papers rather than treating it as current practice.
Theory now or theory later?
- Later (most learners): build projects first with Hands-On Machine Learning, adding Bishop's Pattern Recognition and Machine Learning when you need to understand why models behave as they do.
- Now (math-heavy backgrounds): if you already know calculus and probability, The Elements of Statistical Learning gives the rigorous statistical view; the authors provide the PDF free on Stanford's site at web.stanford.edu/~hastie/ElemStatLearn.
- Either way: supplement with Google's free Machine Learning Crash Course at developers.google.com for interactive exercises between chapters.
What are the most common mistakes when buying ML books?
- Buying three theory books at once. One hands-on plus one theory text is enough to start; finish before expanding.
- Skipping prerequisites. Bishop and ESL assume comfort with matrix notation; struggling through without that base wastes weeks.
- Treating print editions as current. Library versions age; check each book's companion website for updated code repositories.
- Ignoring free legal editions. Three of these four are freely available from their authors — pay only when you want print.
Frequently Asked Questions
Do I need all four books?
No. Most learners need exactly two: Hands-On Machine Learning for practice and one theory reference chosen from the other three based on math comfort.
Are older editions okay?
For ESL and Bishop, yes — the material is stable. For Hands-On Machine Learning, buy the latest edition because the library APIs it teaches change between editions.
What math do I need before starting?
Linear algebra (matrices, vectors), basic calculus (derivatives, gradients), and introductory probability. The Deep Learning book's early chapters review exactly this material.
Should I learn from books or courses?
Both: courses give structure and deadlines; books give depth. Kaggle's free micro-courses at kaggle.com/learn complement any of these texts with immediate coding practice.
Sources
- Deep Learning (free official edition): deeplearningbook.org
- The Elements of Statistical Learning (authors' page): web.stanford.edu/~hastie/ElemStatLearn
- Pattern Recognition and Machine Learning (Microsoft Research): microsoft.com
- scikit-learn — Official documentation: scikit-learn.org
- Google — Machine Learning Crash Course: developers.google.com
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