Hands-On Machine Learning with Scikit-Learn and TensorFlow
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. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aur'lien G'ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to 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. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
Stats for nerds
16recommend
21mentions
18people
2016–2023years
86% recommend14% neutral
Mentions per year
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- If they want to get started with ML, I recommend Hands On Machine Learning with Scikit Learn and Tensorflow.
- Bear in mind Elements of Statistical Learning is a grad-level text. I would never recommend that to a beginner to the field over an Introduction to Statistical Inference, by the same authors.
Geron Aurelien's Oreilly book is great - Hands-On Machine Learning with Scikit-Learn and TensorFlow. Get the second edition which covers Tensorflow 2.
- I'm reading "Hands-On Machine Learning with Scikit-Learn & Tensorflow." It's really good at explaining the theory of how it works and providing examples of what choices to make when using tf.
- Hands-on Machine Learning with Scikit-Learn and TensorFlow [1] is more ML focused, but highly recommended. Out of the three books (Python for Data Analysis and Python Data Science Handbook) I learned the most from this one by far.
- I would pair [1] with Hands On Machine Learning with Scikit-Learn and Tensorflow by Aurélien Géron (I own both). It gives an excellent overview of machine learning including non-deep stuff (plus the ins and outs of scikit-learn and tensorflow).
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