{ "products": [ { "slug": "hands-on-machine-learning", "name": "Hands-On Machine Learning", "kind": "book", "website": "https://openlibrary.org/works/OL20709638W", "page": "https://listme.name/books/hands-on-machine-learning", "tagline": "Practical machine learning with scikit-learn, Keras and TensorFlow", "summary": "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a book by Aurélien Géron, published by O'Reilly Media. Its subtitle is Concepts, Tools, and Techniques to Build Intelligent Systems. The 2022 O'Reilly editions in the Open Library record run to about 850 pages.", "developer": "O'Reilly Media", "author": "Aurélien Géron", "formats": [ "print" ], "pricing": "paid", "price_note": "Print editions from O'Reilly; price varies by seller", "free_to_use": false, "open_source": false, "strengths": [ "Teaches methods through code", "Covers scikit-learn, Keras and TensorFlow", "Recent 2022 edition" ], "limits": [ "Less mathematical depth than the theory texts", "No free full text online" ], "checked": "2026-10-10", "connected_to_publisher": false, "cover": "https://listme.name/assets/covers/hands-on-machine-learning.webp", "overview": "The book teaches machine learning in Python with three libraries. Scikit-learn covers classical models, and Keras and TensorFlow cover neural networks, so readers see both kinds of method in code. The subtitle frames it as concepts, tools and techniques for building intelligent systems, and the hands-on method is central to how it teaches.\n\nO'Reilly publishes it in print, and the 2022 editions in the Open Library record run to about 850 pages. It is not free to read online. The book gives less mathematical depth than the theory texts in this list, but it is more practical than most of them, which makes it a useful companion to Deep Learning and Probabilistic Machine Learning." }, { "slug": "introduction-to-statistical-learning", "name": "An Introduction to Statistical Learning (Python edition)", "kind": "book", "website": "https://www.statlearning.com/", "page": "https://listme.name/books/introduction-to-statistical-learning", "tagline": "Accessible statistical learning textbook with Python labs and a free PDF", "summary": "The Python edition of An Introduction to Statistical Learning (2023) is written by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor. It covers regression, classification, resampling, tree methods and support vector machines, with a Python lab in each chapter. A free PDF is available.", "author": "Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor", "formats": [ "pdf", "print" ], "pricing": "free", "price_note": "Free PDF download; printed copies sold separately", "free_to_use": true, "open_source": false, "strengths": [ "Free PDF of the full text", "Python labs at the end of each chapter", "Written by five statisticians from three universities" ], "limits": [ "Less mathematical depth than graduate texts", "The labs assume some Python skill" ], "checked": "2026-10-10", "connected_to_publisher": false, "overview": "The book covers the core methods of statistical learning, from linear regression and classification through resampling, tree-based methods, support vector machines, survival analysis and unsupervised learning. Each chapter ends with a lab that runs the methods in Python, so readers can run code as they read.\n\nThe site describes the book as a broad, less technical treatment of key topics, and it is written for anyone who wants to use contemporary tools for data analysis. Readers who want derivations and proofs will need a more mathematical text. The authors come from Stanford, Emory and the University of Washington, and the site links errata and a reviews page." } ], "differences": [ "An Introduction to Statistical Learning (Python edition) has a free plan or free version; Hands-On Machine Learning does not.", "Hands-On Machine Learning: paid. An Introduction to Statistical Learning (Python edition): free." ], "shared_rankings": [ { "list": "ai-and-machine-learning-books", "name": "AI and machine learning books", "page": "https://listme.name/best/ai-and-machine-learning-books", "of": 13, "results": [ { "product": "hands-on-machine-learning", "rank": 6, "score": 87, "verdict": "Practical code for classical models and neural networks, built on scikit-learn, Keras and TensorFlow. It is paid with no free text, and it goes less deep into theory than the books above it.", "best_for": "Building models with scikit-learn and TensorFlow", "spec": { "level": "Intermediate", "free_online": false, "latest_edition": "2022, O'Reilly", "math_depth": "Light: basic Python and algebra" } }, { "product": "introduction-to-statistical-learning", "rank": 4, "score": 90, "verdict": "The most approachable text here, with Python labs and a free PDF. It stops short of the mathematics in the Murphy and Bishop books, which suits readers starting out.", "best_for": "Statistical learning for beginners with Python", "spec": { "level": "Beginner", "free_online": true, "latest_edition": "2023, Python edition", "math_depth": "Light: basic statistics" } } ] } ], "ranks_higher_in_closest_shared_list": "introduction-to-statistical-learning", "page": "https://listme.name/compare/hands-on-machine-learning-vs-introduction-to-statistical-learning", "json": "https://listme.name/api/v1/compare/hands-on-machine-learning-vs-introduction-to-statistical-learning.json", "notes": [ "Scores are editorial and only comparable inside one list. Products with connected_to_publisher true receive a disclosed ranking lift: https://listme.name/methodology" ], "source": "https://listme.name", "license": "Free to read and cite with attribution to listme.name.", "docs": "https://listme.name/developers" }