<commit_before>===========================================
External Resources, Videos and Talks
===========================================

For written tutorials, see the :ref:`Tutorial section <tutorial_menu>` of
the documentation.

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.  A basic
understanding of NumPy arrays is recommended to make the most of scikit-learn.

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/>`_

.. _videos:

Videos
======

- `Introduction to scikit-learn
  <http://videolectures.net/icml2010_varaquaux_scik/>`_ by `<NAME>`_ at
  ICML 2010

    A three minute video from a very early stage of the scikit, explaining the
    basic idea and approach we are following.

- `Introduction to statistical learning with scikit-learn <http://archive.org/search.php?query=scikit-learn>`_
  by `<NAME>`_ at SciPy 2011

    An extensive tutorial, consisting of four sessions of one hour.
    The tutorial covers the basics of machine learning,
    many algorithms and how to apply them using scikit-learn. The<commit_msg>Fix Machine Learning for NeuroImaging in Python link.<commit_after>===========================================
External Resources, Videos and Talks
===========================================

For written tutorials, see the :ref:`Tutorial section <tutorial_menu>` of
the documentation.

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.  A basic
understanding of NumPy arrays is recommended to make the most of scikit-learn.

External Tutorials
===================

There are several online tutorials available which are geared toward
specific subject areas:

- `Machine Learning for NeuroImaging in Python <http://nilearn.github.io/>`_
- `Machine Learning for Astronomical Data Analysis <http://astroml.github.com/sklearn_tutorial/>`_

.. _videos:

Videos
======

- `Introduction to scikit-learn
  <http://videolectures.net/icml2010_varaquaux_scik/>`_ by `<NAME>`_ at
  ICML 2010

    A three minute video from a very early stage of the scikit, explaining the
    basic idea and approach we are following.

- `Introduction to statistical learning with scikit-learn <http://archive.org/search.php?query=scikit-learn>`_
  by `<NAME>`_ at SciPy 2011

    An extensive tutorial, consisting of four sessions of one hour.
    The tutorial covers the basics of machine learning,
    many algorithms and how to apply them using scikit-learn. The
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