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https://github.com/ageron/handson-ml
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
https://github.com/ageron/handson-ml
deep-learning deprecated distributed jupyter-notebook machine-learning ml neural-network python scikit-learn tensorflow
Last synced: 3 days ago
JSON representation
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
- Host: GitHub
- URL: https://github.com/ageron/handson-ml
- Owner: ageron
- License: apache-2.0
- Created: 2016-02-16T19:48:39.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2023-10-03T23:44:04.000Z (about 1 year ago)
- Last Synced: 2024-12-02T19:07:17.155Z (10 days ago)
- Topics: deep-learning, deprecated, distributed, jupyter-notebook, machine-learning, ml, neural-network, python, scikit-learn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 83.2 MB
- Stars: 25,219
- Watchers: 1,085
- Forks: 12,912
- Open Issues: 144
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-starred - handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow. (Jupyter Notebook)
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- awesome-list - Machine Learning Notebooks - This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in my O'Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow. (Machine Learning Tutorials / Data Management)
- Awesome-Earth-Artificial-Intelligence - Fundamentals of ML and DL in Python - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow. (Courses)
- ultimate-awesome - Hands-On Machine Learning with Scikit-Learn & TensorFlow
README
Machine Learning Notebooks
==========================# ⚠ THE THIRD EDITION OF MY BOOK IS NOW AVAILABLE.
This project is for the first edition, which is now outdated.
This project aims at teaching you the fundamentals of Machine Learning in
python. It contains the example code and solutions to the exercises in my O'Reilly book [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://learning.oreilly.com/library/view/hands-on-machine-learning/9781491962282/):[![book](http://akamaicovers.oreilly.com/images/9781491962282/cat.gif)](https://learning.oreilly.com/library/view/hands-on-machine-learning/9781491962282/)
## Quick Start
### Want to play with these notebooks online without having to install anything?
Use any of the following services.**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.
* **Recommended**: open this repository in [Colaboratory](https://colab.research.google.com/github/ageron/handson-ml/blob/master/):
* Or open it in [Binder](https://mybinder.org/v2/gh/ageron/handson-ml/master):
* _Note_: Most of the time, Binder starts up quickly and works great, but when handson-ml is updated, Binder creates a new environment from scratch, and this can take quite some time.
* Or open it in [Deepnote](https://beta.deepnote.com/launch?template=data-science&url=https%3A//github.com/ageron/handson-ml/blob/master/index.ipynb):
### Just want to quickly look at some notebooks, without executing any code?
Browse this repository using [jupyter.org's notebook viewer](https://nbviewer.jupyter.org/github/ageron/handson-ml/blob/master/index.ipynb):
_Note_: [github.com's notebook viewer](index.ipynb) also works but it is slower and the math equations are not always displayed correctly.
### Want to run this project using a Docker image?
Read the [Docker instructions](https://github.com/ageron/handson-ml/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-ml.git
$ cd handson-mlNext, run the following commands:
$ conda env create -f environment.yml
$ conda activate tf1
$ python -m ipykernel install --user --name=python3Finally, 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 3.7. 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 3.8 or 3.9 yet, which is why I recommend Python 3.7.
**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\ 3.7/Install\ Certificates.command` in a terminal (change `3.7` 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-ml/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.