Hands-On Machine Learning (3rd edition) is the companion notebook repository for the O’Reilly book by Aurélien Géron. It provides complete, runnable Jupyter notebooks for every chapter — covering the full arc from classical ML with Scikit-Learn through modern deep learning with Keras and TensorFlow 2. Each notebook includes working code, visualizations, and exercise solutions.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/ageron/handson-ml3/llms.txt
Use this file to discover all available pages before exploring further.
Quickstart
Run notebooks in the cloud in one click — no installation needed
Installation
Set up a local environment with Anaconda and Jupyter
Part I: ML Fundamentals
Classification, SVMs, trees, ensembles, and unsupervised learning
Part II: Deep Learning
Neural networks, CNNs, RNNs, transformers, and generative models
What’s inside
The repository contains 19 main notebooks — one per book chapter — plus supplementary tutorials on scientific Python and math prerequisites.Scikit-Learn
End-to-end ML projects, classification, regression, and clustering
TensorFlow & Keras
Build, train, and tune neural networks with the Keras API
Computer Vision
Image classification and object detection with CNNs
NLP & Transformers
Sequence models, attention mechanisms, and language models
Generative Models
Autoencoders, GANs, and diffusion models
Production ML
Deploy models at scale with Vertex AI and TensorFlow Serving
Run notebooks instantly
Open in Google Colab
The recommended way to run notebooks — no setup required
Open in Kaggle
Run notebooks directly in Kaggle’s free environment
Getting started locally
If you have a TensorFlow-compatible NVIDIA GPU, see the installation guide for GPU setup instructions including CUDA and cuDNN.
Notebook overview
| # | Chapter | Topics |
|---|---|---|
| 1 | The ML Landscape | Types of ML, model selection, challenges |
| 2 | End-to-End ML Project | Pipeline, feature engineering, evaluation |
| 3 | Classification | Binary/multiclass, precision/recall, ROC |
| 4 | Training Models | Linear/polynomial regression, regularization |
| 5 | Support Vector Machines | Linear SVM, kernel trick, SVR |
| 6 | Decision Trees | CART, pruning, visualization |
| 7 | Ensemble Learning | Bagging, boosting, XGBoost |
| 8 | Dimensionality Reduction | PCA, t-SNE, UMAP |
| 9 | Unsupervised Learning | K-Means, DBSCAN, Gaussian mixtures |
| 10 | Neural Nets with Keras | Sequential/Functional API, callbacks |
| 11 | Training Deep Networks | Batch norm, dropout, learning schedules |
| 12 | Custom TF Models | Custom layers, training loops, autodiff |
| 13 | Data Pipelines | tf.data, TF Datasets, preprocessing layers |
| 14 | Computer Vision CNNs | ResNet, EfficientNet, object detection |
| 15 | Sequences & Time Series | RNNs, LSTMs, WaveNet, ARMA |
| 16 | NLP & Attention | Transformers, BERT, T5, translation |
| 17 | Autoencoders & GANs | VAEs, StyleGAN, DDPM diffusion |
| 18 | Reinforcement Learning | Policy gradients, DQN, Gymnasium |
| 19 | Deploy at Scale | Vertex AI, TF Serving, distributed training |