38 lines
1.3 KiB
ReStructuredText
38 lines
1.3 KiB
ReStructuredText
.. _stat_learn_tut_index:
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==========================================================================
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A tutorial on statistical-learning for scientific data processing
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==========================================================================
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.. topic:: Statistical learning
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`Machine learning <https://en.wikipedia.org/wiki/Machine_learning>`_ is
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a technique with a growing importance, as the
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size of the datasets experimental sciences are facing is rapidly
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growing. Problems it tackles range from building a prediction function
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linking different observations, to classifying observations, or
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learning the structure in an unlabeled dataset.
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This tutorial will explore *statistical learning*, the use of
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machine learning techniques with the goal of `statistical inference
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<https://en.wikipedia.org/wiki/Statistical_inference>`_:
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drawing conclusions on the data at hand.
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Scikit-learn is a Python module integrating classic machine
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learning algorithms in the tightly-knit world of scientific Python
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packages (`NumPy <http://www.scipy.org>`_, `SciPy
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<http://www.scipy.org>`_, `matplotlib
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<http://matplotlib.org>`_).
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.. include:: ../../includes/big_toc_css.rst
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.. toctree::
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:maxdepth: 2
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settings
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supervised_learning
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model_selection
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unsupervised_learning
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putting_together
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finding_help
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