Documentation of scikit-learn 0.20.2
A very short introduction into machine learning
problems and how to solve them using scikit-learn.
Presents basic concepts and conventions.
The main documentation. This contains an
in-depth description of all algorithms and how
to apply them.
Useful tutorials for developing a feel
for some of scikit-learn's applications in the
machine learning field.
The definitive description of key concepts
and API elements for using scikit-learn and developing compatible tools.
The exact API of all functions and classes, as given by the docstrings.
The API documents expected types and allowed features for all functions,
and all parameters available for the algorithms.
Information on how to contribute. This also
contains useful information for advanced users, for example
how to build their own estimators.
Frequently asked questions about the project and contributing.
Talks given, slide-sets and other information relevant to scikit-learn.
A graphical overview of basic areas of machine
learning, and guidance which kind of algorithms
to use in a given situation.
Other machine learning packages for Python and
related projects. Also algorithms that are slightly out of
scope or not well established enough for scikit-learn.
