Scikit-learn-tutorial is a project mainly written in Python, it's free.
Applied Machine Learning in Python with scikit-learn
.. -- mode: rst --
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.
.. 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
The prebuilt HTML version is published as a github pages:
http://scikit-learn.github.com/scikit-learn-tutorial
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 named 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).