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
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.
Online HTML version
The prebuilt HTML version is at:
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
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.
The example snippets in the rST source files can be tested with nose:
$ nosetests -s --with-doctest --doctest-tests --doctest-extension=rst
Contact the developers
If you have questions about this tutorial you can ask them on the
scikit-learn mailing list on sourceforge:
Some developers tend to hang around the channel
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 example code and solutions to exercises are
distributed under the same license as the