Material for the MASH course on introduction to scikit-learn
Jupyter Notebook Python
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README.md

Introduction to scikit-learn

Master M2: Mathématiques, Apprentissage et Sciences Humaines (MASH) 2016-2017 course

Instructors: Fabian Pedregosa and Fajwel Fogel.

This repository contains notebooks and other files associated with the MASH course introduction to Scikit-learn.

Notebook Listing

You can view the teaching materials using the excellent nbviewer service.

Note, however, that you cannot modify or run the contents within nbviewer. To modify them, first download the tutorial repository, change to the notebooks directory, and run ipython notebook. You should see the list in the ipython notebook launch page in your web browser. For more information on the IPython notebook, see http://ipython.org/notebook.html

Note also that some of the code in these notebooks will not work outside the directory structure of this tutorial, so it is important to clone the full repository if possible.

Installation Notes

This tutorial requires the following packages:

The easiest way to get these is to use the conda environment manager. I suggest downloading and installing miniconda.

Once this is installed, the following command will install all required packages in your Python environment:

$ conda install numpy scipy matplotlib scikit-learn jupyter seaborn plotly

Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/.

Downloading the Tutorial Materials

I would highly recommend using git, not only for this tutorial, but for the general betterment of your life. Once git is installed, you can clone the material in this tutorial by using the git address shown above:

git clone https://github.com/fabianp/mash_sklearn_intro.git

If you can't or don't want to install git, there is a link above to download the contents of this repository as a zip file. I may make minor changes to the repository in the days before the tutorial, however, so cloning the repository is a much better option.

Other resources

Material from last year's course

See also the excellent scikit-learn tutorial by Jake Vanderplas.