.. _stat_learn_tut_index:

==========================================================================
A tutorial on statistical-learning for scientific data processing
==========================================================================

.. topic:: Statistical learning 

    `Machine learning <https://en.wikipedia.org/wiki/Machine_learning>`_ is
    a technique with a growing importance, as the
    size of the datasets experimental sciences are facing is rapidly
    growing. Problems it tackles range from building a prediction function
    linking different observations, to classifying observations, or
    learning the structure in an unlabeled dataset. 
    
    This tutorial will explore *statistical learning*, the use of
    machine learning techniques with the goal of `statistical inference 
    <https://en.wikipedia.org/wiki/Statistical_inference>`_:
    drawing conclusions on the data at hand.

    Scikit-learn is a Python module integrating classic machine
    learning algorithms in the tightly-knit world of scientific Python
    packages (`NumPy <https://www.numpy.org/>`_, `SciPy
    <https://scipy.org/>`_, `matplotlib
    <https://matplotlib.org/>`_).

.. include:: ../../includes/big_toc_css.rst

.. toctree::
   :maxdepth: 2

   settings
   supervised_learning
   model_selection
   unsupervised_learning
   putting_together
