{ "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": "probabilistic-machine-learning", "name": "Probabilistic Machine Learning: An Introduction", "kind": "book", "website": "https://probml.github.io/pml-book/book1.html", "page": "https://listme.name/books/probabilistic-machine-learning", "tagline": "Probability-based machine learning textbook with a free draft PDF", "summary": "Probabilistic Machine Learning: An Introduction is a 2022 MIT Press textbook by Kevin P. Murphy. It builds machine learning on probability and Bayesian reasoning, with figures that come with reproducible code. A free draft PDF is published under a non-commercial, no-derivatives license.", "author": "Kevin P. Murphy", "formats": [ "pdf", "print" ], "pricing": "free", "price_note": "Free draft PDF; hardcopy sold by MIT Press", "free_to_use": true, "open_source": false, "strengths": [ "Free draft PDF and reproducible figure code", "One probabilistic framework for many models", "Published in 2022, after the author's 2012 text" ], "limits": [ "Long and mathematical for self-study", "Free draft carries a non-commercial, no-derivatives license" ], "checked": "2026-10-10", "connected_to_publisher": false, "cover": "https://listme.name/assets/covers/probabilistic-machine-learning.webp", "overview": "The book covers supervised and unsupervised learning, state-space models and deep learning under one probabilistic framework. Each model uses the same notation for inference and uncertainty, which makes the methods easier to compare. The site links a short and a long table of contents and a diff against the author's 2012 book.\n\nThe draft PDF is free to download, and the author asks readers to report errors through the site. Colab notebooks reproduce most figures, which helps readers who want to test the methods. The text assumes calculus, linear algebra and some probability, so it suits advanced undergraduates and graduate students more than readers new to mathematics." } ], "differences": [ "Probabilistic Machine Learning: An Introduction has a free plan or free version; Hands-On Machine Learning does not.", "Hands-On Machine Learning: paid. Probabilistic Machine Learning: An Introduction: 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": "probabilistic-machine-learning", "rank": 2, "score": 92.5, "verdict": "A broad, current treatment of machine learning as probabilistic modeling, with a free draft PDF. It is more mathematical than the practical texts here and newer than Bishop's 2006 book.", "best_for": "Probabilistic modeling and Bayesian methods", "spec": { "level": "Advanced", "free_online": true, "latest_edition": "2022, MIT Press; draft PDF updated", "math_depth": "Heavy: probability and calculus" } } ] } ], "ranks_higher_in_closest_shared_list": "probabilistic-machine-learning", "page": "https://listme.name/compare/hands-on-machine-learning-vs-probabilistic-machine-learning", "json": "https://listme.name/api/v1/compare/hands-on-machine-learning-vs-probabilistic-machine-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" }