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ageron/handson-ml2

★ 29,932Jupyter NotebookAudience · vibe coderComplexity · 2/5MaintainedLicenseSetup · moderate

TLDR

Interactive Jupyter notebooks teaching machine learning fundamentals with Python, Scikit-Learn, Keras, and TensorFlow. Companion to the 2019 book edition (now superseded by a third edition).

Mindmap

mindmap
  root((repo))
    What it does
      Teaches ML basics
      Neural networks
      Pattern recognition
    Content format
      Jupyter Notebooks
      Code examples
      Exercise solutions
    Tech stack
      Python
      Scikit-Learn
      Keras
      TensorFlow
    Use cases
      Learn ML fundamentals
      Work through textbook
      Run code online free
    Audience
      Beginners
      Students
      Book readers

Things people build with this

USE CASE 1

Work through the second edition of the Hands-on Machine Learning textbook with runnable code examples.

USE CASE 2

Learn machine learning fundamentals including supervised learning, neural networks, and data preprocessing.

USE CASE 3

Run interactive notebooks in your browser without installing Python or libraries locally.

Tech stack

PythonJupyter NotebookScikit-LearnKerasTensorFlow

Getting it running

Difficulty · moderate Time to first run · 30min

TensorFlow and Keras installation can be slow; requires Python 3.6+ and pip/conda environment setup.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

Handson-ml2 is the companion repository for the second edition of the book "Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow," published in 2019. It contains Jupyter Notebooks, interactive documents that mix explanations, code, and results, covering the fundamentals of machine learning in Python. Note that this edition is now marked as outdated by its author, who has released a third edition; this repository is preserved for reference. The notebooks walk through core machine learning concepts: training models to recognize patterns in data, building neural networks (software systems loosely inspired by the brain), and using established libraries to handle common tasks. Learners can run the notebooks online for free without installing anything, using services that provide computing resources in the browser. Someone would use this repository if they are working through the second edition of the book and need the code examples and exercise solutions. For anyone starting fresh, the author recommends the newer third edition instead.

Copy-paste prompts

Prompt 1
Show me how to build a simple neural network using Keras from the handson-ml2 notebooks.
Prompt 2
Walk me through the Scikit-Learn examples in handson-ml2 for training a classification model.
Prompt 3
How do I use the handson-ml2 notebooks to understand how to preprocess data for machine learning?
Prompt 4
Explain the TensorFlow examples in handson-ml2 for building and training deep learning models.
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