ageron/handson-ml3

★ 13,162Jupyter NotebookAudience · dataComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((repo))
    What it does
      Book code notebooks
      Exercise solutions
      Deep learning examples
    Tech Stack
      Python
      Scikit-Learn
      Keras and TensorFlow
    How to run
      Google Colab browser
      Local Conda setup
      Kaggle or Binder
    Audience
      ML beginners
      Data science students

mindmap root((repo)) What it does Book code notebooks Exercise solutions Deep learning examples Tech Stack Python Scikit-Learn Keras and TensorFlow How to run Google Colab browser Local Conda setup Kaggle or Binder Audience ML beginners Data science students

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Things people build with this

USE CASE 1

Work through hands-on machine learning exercises from the book with runnable code, all in a browser via Google Colab at no cost.

USE CASE 2

Study neural network implementations in Keras and TensorFlow using the book's example notebooks side by side with the text.

USE CASE 3

Check your answers to the book's exercises using the included solution notebooks.

USE CASE 4

Set up a local Python environment with optional GPU support for training the book's deep learning models faster.

Tech stack

PythonJupyter NotebookScikit-LearnKerasTensorFlowGoogle Colab

Getting it running

Difficulty · easy Time to first run · 5min

No setup needed via Google Colab, click the Colab link in the README to run any notebook instantly in your browser.

In plain English

This repository contains the Jupyter Notebooks from the third edition of the book "Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow" by Aurelien Geron, published by O'Reilly. The notebooks are the practical, code-alongside-text companion to that book. If you own or are studying from the third edition, this is where you find the example code and the solutions to the exercises. Machine learning is the field of getting software to learn patterns from data rather than following explicit rules. The book and notebooks cover the fundamentals using three Python tools that are widely used in the field: Scikit-Learn for classic machine learning, and Keras and TensorFlow for deep learning (neural networks). The content is aimed at people who want hands-on experience working through concrete examples in Python. You do not need to install anything to get started. The notebooks can be opened and run in the browser via Google Colab, which provides free cloud computing. Kaggle, Binder, and Deepnote are listed as alternative online environments that may also work. If you want to just read the notebooks without running code, a static viewer is also available. For local installation, the README recommends using Anaconda or Miniconda (Python environment tools) along with Git to clone the repository. The steps involve creating a dedicated Python environment from a provided configuration file and then launching Jupyter Notebook. If you have a compatible graphics card you can also set up GPU support for faster training. A detailed installation guide covers edge cases like SSL certificate errors on macOS. The repository accompanies the third edition specifically. Previous editions have separate repositories linked from the README. Python 3.10 is recommended, though any version 3.7 or newer is expected to work. The project was supported in part by Google Cloud credits and has received contributions from multiple reviewers and developers.

Copy-paste prompts

Prompt 1

I'm following Hands-On ML3 chapter 2. Open the housing prices prediction notebook and walk me through the data preprocessing steps.

Prompt 2

Using the handson-ml3 notebooks as a guide, help me train a simple neural network with Keras on my own dataset and evaluate its accuracy.

Prompt 3

Help me set up a local Conda environment to run all the handson-ml3 notebooks on my laptop without a GPU.

Prompt 4

In the handson-ml3 style, write a Scikit-Learn pipeline that preprocesses my CSV data and trains a Random Forest classifier.

Prompt 5

I finished chapter 4 of Hands-On ML3. What are the exercise solutions for the linear regression exercises in that chapter?

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