Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/inria/scikit-learn-mooc

Machine learning in Python with scikit-learn MOOC
https://github.com/inria/scikit-learn-mooc

machine-learning mooc python scikit-learn

Last synced: 6 days ago
JSON representation

Machine learning in Python with scikit-learn MOOC

Awesome Lists containing this project

README

        

# scikit-learn course

📢 📢 📢 A new session of the [Machine learning in Python with scikit-learn
MOOC](https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn),
is available starting on November 8th, 2023 and will remain open on self-paced
mode. Enroll for the full MOOC experience (quiz solutions, executable
notebooks, discussion forum, etc ...) !

The MOOC is free and hosted on the [FUN-MOOC](https://fun-mooc.fr/) platform
which does not use the student data for any other purpose than improving the
educational material.

The static version of the course can be browsed online: https://inria.github.io/scikit-learn-mooc

## Course description

The course description can be found here:
https://inria.github.io/scikit-learn-mooc/index.html

## Follow the course online

A few different ways are available:
- Launch an online notebook environment using [![Binder](https://mybinder.org/badge_logo.svg)](
https://mybinder.org/v2/gh/INRIA/scikit-learn-mooc/main?filepath=full-index.ipynb)
- Browse [website](https://inria.github.io/scikit-learn-mooc) generated with
[Jupyter Book](https://jupyterbook.org/)

## Running the notebooks locally

See instructions [here](./local-install-instructions.md)

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md)

## How to cite us

The MOOC material is developed publicly under the [CC-BY
license](https://github.com/INRIA/scikit-learn-mooc/blob/main/LICENSE).

You can cite us through the project's Zenodo archive using the following DOI:
[10.5281/zenodo.7220306](https://doi.org/10.5281/zenodo.7220306).