scikit-learn/doc/tutorial/index.rst

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.. _tutorial_menu:
.. include:: ../includes/big_toc_css.rst
Tutorials: From the bottom up with scikit-learn
=======================================================================
.. topic:: New to Scientific Python?
For those that are still new to the scientific Python ecosystem,
we highly recommend the `Python Scientific Lecture
Notes <http://scipy-lectures.github.io/>`_. This will help you
find your footing a bit and will definitely improve your
scikit-learn experience.
.. topic:: Quick start
In this section, we introduce the `machine learning
<http://en.wikipedia.org/wiki/Machine_learning>`_
vocabulary that we use through-out `scikit-learn` and give a
simple learning example.
.. toctree::
:maxdepth: 2
basic/tutorial.rst
.. topic:: Statistical-learning Tutorial
This tutorial covers some of the models and tools available
to do data-processing with Scikit Learn and how to learn from
your data.
.. toctree::
:maxdepth: 2
statistical_inference/index.rst
.. topic:: **Machine Learning Cheat Sheet (for scikit-learn)**
This flowchart is useful for newcomers regarding how to go about
solving problems using scikit-learn. It provides a rough guide on
how to approach problems and which estimators to try out on your
data. Click the image below to begin..
.. toctree::
:maxdepth: 2
machine_learning_map/index.rst
.. topic:: **External Tutorials**
There are several online tutorials available which are geared toward
specific subject areas:
- `Machine Learning for NeuroImaging in Python <http://nisl.github.com/>`_
- `Machine Learning for Astronomical Data Analysis <http://astroml.github.com/sklearn_tutorial/>`_
.. topic:: **Videos**
Videos with tutorials can also be found in the :ref:`videos` section.
.. note:: **Doctest Mode**
The code-examples in the above tutorials are written in a
*python-console* format. If you wish to easily execute these examples
in **iPython**, use::
%doctest_mode
in the iPython-console. You can then simply copy and paste the examples
directly into iPython without having to worry about removing the **>>>**
manually.