scikit-learn is a python module for machine learning built on top of numpy / scipy.

The purpose of the scikit-learn-tutorial subproject is to learn how to apply machine learning to practical situations using the algorithms implemented in the scikit-learn library.

The target audience is experienced Python developers familiar with numpy and scipy.

Prebuilt versions of this tutorial are available from the `github download page`_.

While following the exercices you might find helpful to use the official scikit-learn user guide (PDF) as a more comprehensive reference:

If you need a numpy refresher please first have a look at the Scientific Python lecture notes (PDF), esp. chapter 4.

The project is hosted on github at https://github.com/scikit-learn/scikit-learn-tutorial

You can build the HTML and PDF (requires pdflatex) versions of this tutorial by installing sphinx (1.0.0+):

$ sudo pip install -U sphinx

Then for the html variant:

$ cd tutorial
$ make html

The results is available in the _build/html/ subdolder. Point your browser to the index.html file for table of content.

To build the PDF variant:

$ make latex
$ cd _build/latex
$ pdflatex scikit_learn_tutorial.tex

You should get a file names scikit_learn_tutorial.pdf as output.

If you have questions about this tutorial you can ask them on the scikit-learn mailing list on sourceforge: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general

Some developers tend to hang around the channel #scikit-learn at irc.freenode.net, especially during the week preparing a new release. If nobody is available to answer your questions there don't hesitate to ask it on the mailing list to reach a wider audience.

This tutorial is distributed under the Creative Commons Attribution 3.0 license. The python source code and exercices solutions are distributed under the same license as the scikit-learn project (Simplidied BSD).