Scikit-learn Tutorial for scipy beginners

This repository contains files and other info for a training on data analysis with scikit-learn for people who are not experts in it.

These were originaly associated with the EuroPython 2014 scikit-learn tutorial.

Instructor: Gael Varoquaux @GaelVaroquaux | http://gael-varoquaux.info

Installation Notes

This tutorial will require recent installations of numpy, scipy, matplotlib, scikit-learn.

For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a package such as Anaconda CE, which can be downloaded and installed for free.

Reading the training materials

Not all the material will be covered during the training: there is not enough time available. However, you can follow the material by yourself.

With the IPython notebook

The recommended way to access the materials is to execute them in the IPython notebook. If you have the IPython notebook installed, you should download the materials (see below), go to the notebooks directory, and type:

ipython notebook

in your terminal window. This will open a notebook panel load in your web browser.

On Internet

If you don't have the IPython notebook installed, you can browse the files on Internet:

• For the instructions without the solutions:

  http://nbviewer.ipython.org/github/GaelVaroquaux/sklearn_europython_2014/tree/master/notebooks/

• For the instructions and the solutions:

  http://nbviewer.ipython.org/github/GaelVaroquaux/sklearn_europython_2014/tree/master/rendered_notebooks/

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:

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.

Data Downloads

The data for this tutorial is not included in the repository. We will be using several data sets during the tutorial: most are built-in to scikit-learn, which includes code which automatically downloads and caches these data. Because the wireless network at conferences can often be spotty, it would be a good idea to download these data sets before arriving at the conference. You can do so by using the fetch_data.py included in the tutorial materials.

Original material from the Scipy 2013 tutorial

This material is adapted from the scipy 2013 tutorial:

http://github.com/jakevdp/sklearn_scipy2013

Original authors:

• Gael Varoquaux @GaelVaroquaux | http://gael-varoquaux.info
• Olivier Grisel @ogrisel | http://ogrisel.com
• Jake VanderPlas @jakevdp | http://jakevdp.github.com