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Documentation of scikit-learn 0.17
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                    <h2><a href="tutorial/basic/tutorial.html">Quick Start</a></h2>
                    <blockquote>A very short introduction into machine learning
                    problems and how to solve them using scikit-learn.
                    Introduced basic concepts and conventions.
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                    <h2><a href="user_guide.html">User Guide</a></h2>
                    <blockquote>The main documentation. This contains an
                        in-depth description of all algorithms and how
                        to apply them.
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                    <h2>Other Versions</h2>
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                        <li><a href="http://scikit-learn.org/dev/documentation.html">scikit-learn 0.18 (development)</a></li>
                        <li>scikit-learn 0.17 (stable)</li>
                        <li><a href="http://scikit-learn.org/0.17/documentation.html">scikit-learn 0.16</a></li>
                        <li><a href="http://scikit-learn.org/0.15/documentation.html">scikit-learn 0.15</a></li>
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                    <h2><a href="tutorial/index.html">Tutorials</a></h2>
                    <blockquote>Useful tutorials for developing a feel
                    for some of scikit-learn's applications in the
                    machine learning field.
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                    <h2><a href="modules/classes.html">API</a></h2>
                            <blockquote>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.
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                    <h2><a href="presentations.html">Additional Resources</a></h2>
                                <blockquote>Talks given, slide-sets and other information relevant to scikit-learn.
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                    <h2><a href="developers/index.html">Contributing</a></h2>
                            <blockquote>Information on how to contribute. This also
                            contains useful information for advanced users, for example
                            how to build their own estimators.
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                    <h2><a href="tutorial/machine_learning_map/index.html">Flow Chart</a></h2>
                    <blockquote>A graphical overview of basic areas of machine
                        learning, and guidance which kind of algorithms
                        to use in a given situation.
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                    <h2><a href="faq.html">FAQ</a></h2>
                    <blockquote>Frequently asked questions about the project and contributing.
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                    <h2><a href="related_projects.html">Related packages</a></h2>
                    <blockquote>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.
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