<div dir="ltr"><div><div><div><div>Thanks to everybody to made it out last night! I wanted to follow up with some links:<br><br></div><div>0) SAGE is a Python distribution that frees you from the hassle of worrying about installing dependencies:<br>
<br></div><div><a href="http://www.sagemath.org">http://www.sagemath.org</a><br></div><div><br></div>1) The SAGE tutorial worksheets I created for Numpy+Matplotlib, and arms exports:<br><br><a href="https://github.com/mschachter/arms/tree/master/worksheets">https://github.com/mschachter/arms/tree/master/worksheets</a><br>
<br><div>2) PANDAS<br><br><a href="http://pandas.pydata.org/">http://pandas.pydata.org/</a><br><br></div><div>3) scikit.learn<br><br><a href="http://scikit-learn.org/stable/">http://scikit-learn.org/stable/</a><br></div><br>
</div><div>4) Noisebridge Machine Learning Wiki Page (check out the software list)<br><br><a href="https://www.noisebridge.net/wiki/Machine_Learning">https://www.noisebridge.net/wiki/Machine_Learning</a><br></div><div><br>
</div>5) Free machine learning e-book<br><br><a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/">http://statweb.stanford.edu/~tibs/ElemStatLearn/</a><br><br></div>6) Pattern Recognition and Machine Learning Book<br>
<br></div><div>Google "Pattern Recognition and Machine Learning"<br></div><div><br></div>7) Bay Area Women in Machine Learning<br><br><a href="http://www.meetup.com/Bay-Area-Women-in-Machine-Learning-and-Data-Science/" target="_blank">http://www.meetup.com/Bay-Area-Women-in-Machine-Learning-and-Data-Science/</a><br>
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