This repository provides a series of Jupyter notebooks for learning Machine Learning and Deep Learning fundamentals using Scikit-Learn, Keras, and TensorFlow 2. It's targeted at individuals seeking practical, hands-on experience with these libraries, offering code examples and solutions to exercises from the second edition of the "Hands-On Machine Learning" book.

How It Works

The notebooks guide users through core ML concepts by implementing algorithms and models directly in Python. They leverage popular libraries like Scikit-Learn for traditional ML tasks and Keras/TensorFlow for deep learning, providing a structured learning path from basic principles to more advanced neural network architectures.

Quick Start & Requirements

• Online Execution: Recommended via Google Colab or Kaggle for free GPU/TPU access.
• Local Installation:
  • Install Anaconda/Miniconda, Git.
  • Optional: NVIDIA GPU driver, CUDA, cuDNN for GPU acceleration.
  • Clone the repository: git clone https://github.com/ageron/handson-ml2.git
  • Navigate to the directory: cd handson-ml2
  • Create Conda environment: conda env create -f environment.yml
  • Activate environment: conda activate tf2
  • Install kernel: python -m ipykernel install --user --name=python3
  • Start Jupyter: jupyter notebook
• Recommended Python Version: 3.8 (due to library compatibility).
• Documentation: Detailed installation instructions are available.

Highlighted Details

• Covers both Scikit-Learn for traditional ML and Keras/TensorFlow for deep learning.
• Includes solutions to exercises from the "Hands-On Machine Learning" book.
• Offers Docker support for easier deployment.
• Provides links to online execution environments for quick access.

Maintenance & Community

The project has received significant contributions, with special thanks to Haesun Park and Ian Beauregard for reviews and PRs. Docker support was contributed by Steven Bunkley and Ziembla.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README text. Compatibility for commercial use or closed-source linking would require clarification of the license.

Limitations & Caveats

This repository contains code for the second edition of the book; the third edition (ageron/handson-ml3) is available and recommended for more up-to-date code. Online execution environments are temporary. Python 3.9 and 3.10 may have compatibility issues with some libraries.