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<li><a class="reference internal" href="#">External Resources, Videos and Talks</a><ul>
<li><a class="reference internal" href="#new-to-scientific-python">New to Scientific Python?</a></li>
<li><a class="reference internal" href="#external-tutorials">External Tutorials</a></li>
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<h1>External Resources, Videos and Talks<a class="headerlink" href="#external-resources-videos-and-talks" title="Permalink to this headline">¶</a></h1>
<p>For written tutorials, see the <a class="reference internal" href="tutorial/index.html#tutorial-menu"><span class="std std-ref">Tutorial section</span></a> of
the documentation.</p>
<div class="section" id="new-to-scientific-python">
<h2>New to Scientific Python?<a class="headerlink" href="#new-to-scientific-python" title="Permalink to this headline">¶</a></h2>
<p>For those that are still new to the scientific Python ecosystem, we highly
recommend the <a class="reference external" href="http://www.scipy-lectures.org/">Python Scientific Lecture Notes</a>. This will help you find your footing a
bit and will definitely improve your scikit-learn experience. A basic
understanding of NumPy arrays is recommended to make the most of scikit-learn.</p>
</div>
<div class="section" id="external-tutorials">
<h2>External Tutorials<a class="headerlink" href="#external-tutorials" title="Permalink to this headline">¶</a></h2>
<p>There are several online tutorials available which are geared toward
specific subject areas:</p>
<ul class="simple">
<li><a class="reference external" href="http://nilearn.github.io/">Machine Learning for NeuroImaging in Python</a></li>
<li><a class="reference external" href="https://github.com/astroML/sklearn_tutorial">Machine Learning for Astronomical Data Analysis</a></li>
</ul>
</div>
<div class="section" id="videos">
<span id="id1"></span><h2>Videos<a class="headerlink" href="#videos" title="Permalink to this headline">¶</a></h2>
<ul>
<li><p class="first">An introduction to scikit-learn <a class="reference external" href="https://conference.scipy.org/scipy2013/tutorial_detail.php?id=107">Part
I</a> and
<a class="reference external" href="https://conference.scipy.org/scipy2013/tutorial_detail.php?id=111">Part II</a> at Scipy 2013
by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a>, <a class="reference external" href="http://staff.washington.edu/jakevdp">Jake Vanderplas</a> and <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a>. Notebooks on
<a class="reference external" href="https://github.com/jakevdp/sklearn_scipy2013">github</a>.</p>
</li>
<li><p class="first"><a class="reference external" href="http://videolectures.net/icml2010_varaquaux_scik/">Introduction to scikit-learn</a> by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a> at
ICML 2010</p>
<blockquote>
<div><p>A three minute video from a very early stage of scikit-learn, explaining the
basic idea and approach we are following.</p>
</div></blockquote>
</li>
<li><p class="first"><a class="reference external" href="http://archive.org/search.php?query=scikit-learn">Introduction to statistical learning with scikit-learn</a>
by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a> at SciPy 2011</p>
<blockquote>
<div><p>An extensive tutorial, consisting of four sessions of one hour.
The tutorial covers the basics of machine learning,
many algorithms and how to apply them using scikit-learn. The
material corresponding is now in the scikit-learn documentation
section <a class="reference internal" href="tutorial/statistical_inference/index.html#stat-learn-tut-index"><span class="std std-ref">A tutorial on statistical-learning for scientific data processing</span></a>.</p>
</div></blockquote>
</li>
<li><p class="first"><a class="reference external" href="http://www.pyvideo.org/video/417/pycon-2011--statistical-machine-learning-for-text">Statistical Learning for Text Classification with scikit-learn and NLTK</a>
(and <a class="reference external" href="http://www.slideshare.net/ogrisel/statistical-machine-learning-for-text-classification-with-scikitlearn-and-nltk">slides</a>)
by <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a> at PyCon 2011</p>
<blockquote>
<div><p>Thirty minute introduction to text classification. Explains how to
use NLTK and scikit-learn to solve real-world text classification
tasks and compares against cloud-based solutions.</p>
</div></blockquote>
</li>
<li><p class="first"><a class="reference external" href="https://www.youtube.com/watch?v=Zd5dfooZWG4">Introduction to Interactive Predictive Analytics in Python with scikit-learn</a>
by <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a> at PyCon 2012</p>
<blockquote>
<div><p>3-hours long introduction to prediction tasks using scikit-learn.</p>
</div></blockquote>
</li>
<li><p class="first"><a class="reference external" href="https://newcircle.com/s/post/1152/scikit-learn_machine_learning_in_python">scikit-learn - Machine Learning in Python</a>
by <a class="reference external" href="http://staff.washington.edu/jakevdp">Jake Vanderplas</a> at the 2012 PyData workshop at Google</p>
<blockquote>
<div><p>Interactive demonstration of some scikit-learn features. 75 minutes.</p>
</div></blockquote>
</li>
<li><p class="first"><a class="reference external" href="https://www.youtube.com/watch?v=cHZONQ2-x7I">scikit-learn tutorial</a> by <a class="reference external" href="http://staff.washington.edu/jakevdp">Jake Vanderplas</a> at PyData NYC 2012</p>
<blockquote>
<div><p>Presentation using the online tutorial, 45 minutes.</p>
</div></blockquote>
</li>
</ul>
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