.. We are putting the title as a raw HTML so that it doesn't appear in the contents .. raw:: html <h1>scikits.learn: machine learning in Python</h1> .. only:: html .. |banner1| image:: auto_examples/cluster/images/plot_affinity_propagation.png :height: 150 :target: auto_examples/cluster/plot_affinity_propagation.html .. |banner2| image:: auto_examples/gaussian_process/images/plot_gp_regression.png :height: 150 :target: auto_examples/gaussian_process/plot_gp_regression.html .. |banner3| image:: auto_examples/svm/images/plot_oneclass.png :height: 150 :target: auto_examples/svm/plot_oneclass.html .. |banner4| image:: auto_examples/cluster/images/plot_lena_segmentation.png :height: 150 :target: auto_examples/cluster/plot_lena_segmentation.html .. |center-div| raw:: html <div style="text-align: center; margin: 0px 0 -5px 0;"> .. |end-div| raw:: html </div> |center-div| |banner1| |banner2| |banner3| |banner4| |end-div| .. topic:: Easy-to-use and general-purpose machine learning in Python ``scikits.learn`` is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (`numpy <http://www.scipy.org>`_, `scipy <http://www.scipy.org>`_, `matplotlib <http://matplotlib.sourceforge.net/>`_). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: **machine-learning as a versatile tool for science and engineering**. :Features: * **Solid**: :ref:`supervised-learning`: :ref:`svm`, :ref:`linear_model`. * **Work in progress**: :ref:`unsupervised-learning`: :ref:`clustering`, :ref:`mixture`, manifold learning, :ref:`ICA <ICA>`, :ref:`gaussian_process` * **Planed**: Gaussian graphical models, matrix factorization :License: Open source, commercially usable: **BSD license** (3 clause) .. include:: big_toc_css.rst .. note:: This document describes scikits.learn |release|. For other versions and printable format, see :ref:`documentation_resources`. User Guide ========== .. toctree:: :maxdepth: 2 contents Example Gallery =============== .. toctree:: :maxdepth: 2 auto_examples/index Development =========== .. toctree:: :maxdepth: 2 developers/index performance about