<commit_before>.. _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<commit_msg>DOC recommend understanding NumPy in the tutorial<commit_after>.. _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. A basic understanding of NumPy arrays
    is recommended to make the most of scikit-learn.

.. 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
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