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  • Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems

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Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron (Author) Format: Paperback
4.7 out of 5 stars (75)

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The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.

With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems.

  • Understand ML basics, including concepts like overfitting and hyperparameter tuning
  • Complete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation
  • Learn techniques for unsupervised learning, such as clustering and anomaly detection
  • Build advanced architectures like transformers and diffusion models with PyTorch
  • Harness the power of pretrained models—including LLMs—and learn to fine-tune them
  • Train autonomous agents using reinforcement learning
Read more
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  1. ISBN-13
    979-8341607989
  2. Edition
    1st
  3. Publisher
    O'Reilly Media
  4. Publication date
    December 2, 2025
  5. Language
    English
  6. Dimensions
    7 x 2 x 9.19 inches
  7. Print length
    875 pages
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From the Publisher

Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts

From the Preface

Machine Learning in Your Projects

So, naturally you are excited about machine learning and would love to join the party! Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around?

Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look. With machine learning, you could accomplish the following and much more:

  • Segment customers and find the best marketing strategy for each group.
  • Recommend products for each client based on what similar clients bought.
  • Detect which transactions are likely to be fraudulent.
  • Forecast next year’s revenue.
  • Predict peak workloads and suggest optimal staffing levels.
  • Build a chatbot to assist your customers.

Whatever the reason, you have decided to learn machine learning and implement it in your projects. Great idea!

Objective and Approach

This book assumes that you know close to nothing about machine learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data.

We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the deep learning techniques that regularly win competitions. For this, we will be using Python—the leading language for data science and machine learning—as well as open source and production-ready Python frameworks:

  • Scikit-Learn is very easy to use, yet it implements many machine learning algorithms efficiently, so it makes for a great entry point to learning machine learning. It was created by David Cournapeau in 2007, then led by a team of researchers at the French Institute for Research in Computer Science and Automation (Inria), and recently Probabl.ai.
  • PyTorch is a powerful and flexible library for deep learning. It makes it possible to train and run all sorts of neural networks efficiently, and it can distribute the computations across multiple GPUs (graphics processing units). PyTorch (PT) was developed by Facebook’s AI Research lab (FAIR) and first released in 2016. It evolved from Torch, an older framework coded in Lua. In 2022, PyTorch was transitioned to the PyTorch Foundation, under the Linux Foundation, to promote community-driven development.

We will also use these open source machine learning libraries along the way:

  • XGBoost in Chapter 6 to implement a powerful technique called gradient boosting.
  • Hugging Face libraries in Chapters 13 and 15 to download datasets and pretrained models, including transformer models. Transformers are incredibly powerful and versatile, and they are the main building block of virtually all AI assistants today.
  • Gymnasium in Chapter 19 for reinforcement learning (i.e., training autonomous agents).

The book favors a hands-on approach, growing an intuitive understanding of machine learning through concrete working examples and just a little bit of theory.

Hands-On Machine Learning with Scikit-Learn and PyTorch
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Libraries covered Scikit-Learn and PyTorch Scikit-Learn, Keras, and TensorFlow
Expanded coverage of Transformers, Large Language Models, and Chatbots ✓ no data
Coverage of PPO & RLHF ✓ no data
Vision and multimodal transformers ✓ no data
the Hugging Face ecosystem ✓ no data
Diffusion models ✓ no data

Editorial Reviews

About the Author

Aurélien Géron is a Machine Learning consultant. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, a telecom consulting firm. Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school. A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn’t open on the 2nd jump.

Product details

  • ASIN ‏ : ‎ B0F2SG98Q9
  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ December 2, 2025
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 875 pages
  • ISBN-13 ‏ : ‎ 979-8341607989
  • Item Weight ‏ : ‎ 3.24 pounds
  • Dimensions ‏ : ‎ 7 x 2 x 9.19 inches
  • Best Sellers Rank: #30,592 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.7 out of 5 stars (75)

About the author

Follow authors to get new release updates, plus improved recommendations.
Aurélien Géron

Aurélien Géron

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Aurélien Géron is a Machine Learning consultant. A former Googler, he led the YouTube video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.

Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.

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