.. -- mode: rst --

About

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.

Downloading the PDF

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.

.. _github download page: https://github.com/scikit-learn/scikit-learn-tutorial/archives/master
.. _scikit-learn User Guide (PDF): http://downloads.sourceforge.net/project/scikit-learn/documentation/user_guide-0.7.pdf
.. _Scientific Python lecture notes (PDF): http://scipy-lectures.github.com/_downloads/PythonScientific.pdf

Online HTML version

The prebuilt HTML version is published as a github pages:

http://scikit-learn.github.com/scikit-learn-tutorial

Source code of the tutorial and exercises

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

Building the 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 named scikit_learn_tutorial.pdf as output.

Mailing list

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

IRC channel

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.

License

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).