Machine Learning Notebooks, 3rd edition

This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in the third edition of my O'Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition):

Note: If you are looking for the second edition notebooks, check out ageron/handson-ml2. For the first edition, see ageron/handson-ml.

Quick Start

Want to play with these notebooks online without having to install anything?

• (recommended)

⚠ Colab provides a temporary environment: anything you do will be deleted after a while, so make sure you download any data you care about.

Other services may work as well, but I have not fully tested them:

• 

• 

• 

Just want to quickly look at some notebooks, without executing any code?

• 

• github.com's notebook viewer also works but it's not ideal: it's slower, the math equations are not always displayed correctly, and large notebooks often fail to open.

Want to run this project using a Docker image?

Read the Docker instructions.

Want to install this project on your own machine?

Start by installing Anaconda (or Miniconda), git, and if you have a TensorFlow-compatible GPU, install the GPU driver, as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details).

Next, clone this project by opening a terminal and typing the following commands (do not type the first $ signs on each line, they just indicate that these are terminal commands):

$ git clone https://github.com/ageron/handson-ml3.git
$ cd handson-ml3

Next, run the following commands:

$ conda env create -f environment.yml
$ conda activate homl3
$ python -m ipykernel install --user --name=python3

Finally, start Jupyter:

$ jupyter notebook

If you need further instructions, read the detailed installation instructions.

Tip

If one does not want Anaconda, we can choose to install virtualenv and create an isolated Python environment by

python3 -m pip install --user -U virtualenv
python3 -m virtualenv .venv

Activate environment with

source .venv/bin/activate

or, on Windows

./venv\Scripts\activate

To deactivate this environment later, use

deactivate

Install dependencies by

pip3 install -r requirements.txt

Now we can start Jupyter by typing the following command:

python3 -m ipykernel install --user --name=python3 && jupyter notebook

The first half of the composite commands registers virtualenv to Jupyter and give it a name with python3 A Jupyter server is now running in our terminal, listening to port 8888. We can visit this server by opening our web browser to http://localhost:8888/

FAQ

Which Python version should I use?

I recommend Python 3.10. If you follow the installation instructions above, that's the version you will get. Any version ≥3.7 should work as well.

I'm getting an error when I call load_housing_data()

If you're getting an HTTP error, make sure you're running the exact same code as in the notebook (copy/paste it if needed). If the problem persists, please check your network configuration. If it's an SSL error, see the next question.

I'm getting an SSL error on MacOSX

You probably need to install the SSL certificates (see this StackOverflow question). If you downloaded Python from the official website, then run /Applications/Python\ 3.10/Install\ Certificates.command in a terminal (change 3.10 to whatever version you installed). If you installed Python using MacPorts, run sudo port install curl-ca-bundle in a terminal.

I've installed this project locally. How do I update it to the latest version?

See INSTALL.md

How do I update my Python libraries to the latest versions, when using Anaconda?

See INSTALL.md

Contributors

I would like to thank everyone who contributed to this project, either by providing useful feedback, filing issues or submitting Pull Requests. Special thanks go to Haesun Park and Ian Beauregard who reviewed every notebook and submitted many PRs, including help on some of the exercise solutions. Thanks as well to Steven Bunkley and Ziembla who created the docker directory, and to github user SuperYorio who helped on some exercise solutions. Thanks a lot to Victor Khaustov who submitted plenty of excellent PRs, fixing many errors. And lastly, thanks to Google ML Developer Programs team who supported this work by providing Google Cloud Credit.