handson-ml2

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⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.

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Machine Learning Notebooks
==========================

# ⚠ THIS EDITION IS NOW OUTDATED. THE <a href="https://github.com/ageron/handson-ml3">THIRD EDITION OF MY BOOK</a> IS NOW AVAILABLE, AS WELL A <a href="https://github.com/ageron/handson-mlp">PYTORCH VERSION</a> AND <a href="https://homl.info/">MANY TRANSLATIONS</a>.

This project is for the second edition, which is now outdated (it came out in 2019).

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 second edition of my O'Reilly book [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/):

**Note**: If you are looking for the first edition notebooks, check out [ageron/handson-ml](https://github.com/ageron/handson-ml). For the third edition, check out [ageron/handson-ml3](https://github.com/ageron/handson-ml3).

## Quick Start

### Want to play with these notebooks online without having to install anything?
Use any of the following services (I recommended Colab or Kaggle, since they offer free GPUs and TPUs).

**WARNING**: _Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about._

* <a href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

* <a href="https://homl.info/kaggle/"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open in Kaggle" /></a>

* <a href="https://mybinder.org/v2/gh/ageron/handson-ml2/HEAD?filepath=%2Findex.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Launch binder" /></a>

* <a href="https://homl.info/deepnote/"><img src="https://deepnote.com/buttons/launch-in-deepnote-small.svg" alt="Launch in Deepnote" /></a>

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

* <a href="https://nbviewer.jupyter.org/github/ageron/handson-ml2/blob/master/index.ipynb"><img src="https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg" alt="Render nbviewer" /></a>

* [github.com's notebook viewer](https://github.com/ageron/handson-ml2/blob/master/index.ipynb) 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](https://github.com/ageron/handson-ml2/tree/master/docker).

### Want to install this project on your own machine?

Start by installing [Anaconda](https://www.anaconda.com/distribution/) (or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)), [git](https://git-scm.com/downloads), and if you have a TensorFlow-compatible GPU, install the [GPU driver](https://www.nvidia.com/Download/index.aspx), 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-ml2.git
    $ cd handson-ml2

Next, run the following commands:

$ conda env create -f environment.yml
    $ conda activate tf2
    $ python -m ipykernel install --user --name=python3

Finally, start Jupyter:

$ jupyter notebook

If you need further instructions, read the [detailed installation instructions](INSTALL.md).

# FAQ

**Which Python version should I use?**

I recommend Python 3.8. If you follow the installation instructions above, that's the version you will get. Most code will work with other versions of Python 3, but some libraries do not support Python 3.9 or 3.10 yet, which is why I recommend Python 3.8.

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

Make sure you call `fetch_housing_data()` *before* you 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.

**I'm getting an SSL error on MacOSX**

You probably need to install the SSL certificates (see this [StackOverflow question](https://stackoverflow.com/questions/27835619/urllib-and-ssl-certificate-verify-failed-error)). If you downloaded Python from the official website, then run `/Applications/Python\ 3.8/Install\ Certificates.command` in a terminal (change `3.8` 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](INSTALL.md)

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

See [INSTALL.md](INSTALL.md)

## Contributors
I would like to thank everyone [who contributed to this project](https://github.com/ageron/handson-ml2/graphs/contributors), 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.

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