<story><title>A Course in Machine Learning</title><url>http://ciml.info/</url></story><parent_chain></parent_chain><comment><author>kmax12</author><text>A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problems using data science.&lt;p&gt;One thing I didn&amp;#x27;t see covered in depth here was feature engineering, which is the process of preparing your raw data for the machine learning algorithms. They cover it briefly in the chapter on &amp;quot;Practical Considerations&amp;quot;, but anyone looking to apply ML in the real world should look into feature engineering more on their own.&lt;p&gt;One resource I recommend (and I am biased), is a python library for automated feature engineering called, Featuretools (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;featuretools&amp;#x2F;featuretools&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;featuretools&amp;#x2F;featuretools&amp;#x2F;&lt;/a&gt;). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.featuretools.com&amp;#x2F;demos&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.featuretools.com&amp;#x2F;demos&lt;/a&gt;.</text></comment>
<story><title>A Course in Machine Learning</title><url>http://ciml.info/</url></story><parent_chain></parent_chain><comment><author>jpamata</author><text>With all of these online courses around, I&amp;#x27;m just curious: is there an ML course that teaches you the right model to use? Say for example, the right amount of layers&amp;#x2F;nodes for a neural net? As a newcomer doing one of these machine learning MOOCs on his free time, it seems to me that it&amp;#x27;s about chucking in a load of parameters into a black box hoping for the best.</text></comment>