A very short introduction into machine learning problems and how to solve them using scikit-learn. Introduced basic concepts and conventions.
The main documentation. This contains an in-depth description of all algorithms and how to apply them.
- scikit-learn 0.18 (development)
- scikit-learn 0.16 (stable)
- scikit-learn 0.15
Useful tutorials for developing a feel for some of scikit-learn's applications in the machine learning field.
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.
Talks given, slide-sets and other information relevant to scikit-learn.
Information on how to contribute. This also contains useful information for advanced users, for example how to build their own estimators.
A graphical overview of basic areas of machine learning, and guidance which kind of algorithms to use in a given situation.
Frequently asked questions about the project and contributing.
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.