machine learning · Mathematics
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron — Summary, Key Ideas & Quotes
2019856 pages✦ 3-min Big ideas★ 3.6 (9)
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What is Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow about?
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION: Updated all code to TensorFlow 2Introduced the high-level Keras APINew and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more.
A glimpse inside
Learning by Doing
The book’s core philosophy is that the best way to understand machine learning is by implementing it yourself. Rather than overwhelming readers with mathematical abstraction, Géron uses hands-on projects and real datasets to ground each concept. This approach helps demystify complex ideas, making them tangible and memorable. The exercises and code examples are designed to reinforce learning and provide a toolkit that readers can immediately apply to their own problems.
Intuitive Explanations Over Heavy Math
While machine learning can be mathematically dense, this book prioritizes intuition and practical insight. Géron explains algorithms and techniques in plain language, often using analogies and visualizations. This makes the material accessible to readers with only basic programming and math backgrounds, lowering the barrier to entry for self-learners and professionals from other fields.
Ratings at a glance
Open Library
★ 3.6 / 5
9 ratings
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Get smart in 3 min
6 key ideas, distilled
- 1Learning by Doing
- 2Intuitive Explanations Over Heavy Math
- 3From Classical ML to Deep Learning
- 4Production-Ready Tools and Best Practices
- 5Staying Current with the Ecosystem
Frequently asked
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit