Jupyter notebooks implement and derive machine learning algorithms from first principles using NumPy.
Open source
24 alternativesHandson ML3
A series of Jupyter notebooks teaches machine learning and deep learning in Python with Scikit-Learn, Keras, and TensorFlow.
About Handson ML3
Handson ML3 is an open source repository of Jupyter notebooks that teach the fundamentals of machine learning and deep learning in Python, using Scikit-Learn, Keras, and TensorFlow 2. It contains example code and solutions to the exercises in the third edition of the book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurélien Géron.
The notebooks are numbered by chapter, from the machine learning landscape through end-to-end projects, classification, neural networks, computer vision, sequence models, natural language processing, generative models, reinforcement learning, and training and deployment at scale. The repository also includes separate notebooks on math topics and on the NumPy, pandas, and Matplotlib libraries. The project offers several ways to run the code: Google Colab, a Docker image, or a local Conda environment. Running locally with a TensorFlow-compatible GPU requires installing the matching GPU driver, CUDA, and cuDNN. Colab sessions are temporary, so files should be downloaded if they need to be kept.
Key features
- Numbered Jupyter notebooks covering 19 chapters of machine learning and deep learning
- Example code and solutions to exercises from the third edition book
- Separate notebooks on linear algebra, differential calculus, NumPy, pandas, and Matplotlib
- Docker setup instructions for running the project in a container
- Conda environment file for installing dependencies on a local machine
- Google Colab option for running notebooks without a local installation
Best for: Learners who want hands-on Python code for machine learning and deep learning alongside the third edition of the book.
Good for
- Open source repository
- Notebooks are organized by chapter, following the book's structure
- Offers Docker and Conda setup options in addition to Colab
Watch out for
- Colab provides a temporary environment, and work is deleted after a while
- The GitHub notebook viewer is slower, may display math incorrectly, and often fails to open large notebooks
- Other cloud services may work but have not been fully tested by the author
Handson ML3 at a glance
- Category
- Education & Learning › Learn to Code
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Handson ML3 FAQ
Can I run the notebooks without installing anything?
Yes. The project recommends Google Colab for this. Colab provides a temporary environment, so data you need to keep should be downloaded.
Is there a Docker option?
Yes. The project includes instructions for running it with a Docker image.
Where are the notebooks for earlier editions of the book?
The second edition notebooks are in the ageron/handson-ml2 repository, and the first edition notebooks are in ageron/handson-ml.
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