1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
|
.. _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
|
