<story><title>Introduction to Statistical Learning</title><url>http://faculty.marshall.usc.edu/gareth-james/ISL/</url></story><parent_chain></parent_chain><comment><author>dnquark</author><text>Hastie and Tibshirani teach a free course based on this book on Stanford&amp;#x27;s OpenEdX (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;online.stanford.edu&amp;#x2F;courses&amp;#x2F;sohs-ystatslearning-statistical-learning&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;online.stanford.edu&amp;#x2F;courses&amp;#x2F;sohs-ystatslearning-stat...&lt;/a&gt;). I highly recommend taking this course or reading the book before delving into ESL. IMO, ESL is excellent as a reference, but trying to learn by reading it linearly is not an optimal time investment.&lt;p&gt;Now if only a similar course existed for Wasserman&amp;#x27;s &amp;quot;All of Statistics...&amp;quot;</text></comment>
<story><title>Introduction to Statistical Learning</title><url>http://faculty.marshall.usc.edu/gareth-james/ISL/</url></story><parent_chain></parent_chain><comment><author>ryankupyn</author><text>I&amp;#x27;m a big fan of ISL - one of the best intro machine-learning oriented textbooks out there IMO. If you&amp;#x27;re looking for book that still offers a broad survey while going a bit deeper into the math, I recommend Elements of Statistical Learning as well (they share 2 authors):&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;web.stanford.edu&amp;#x2F;~hastie&amp;#x2F;Papers&amp;#x2F;ESLII.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;web.stanford.edu&amp;#x2F;~hastie&amp;#x2F;Papers&amp;#x2F;ESLII.pdf&lt;/a&gt;</text></comment>