<story><title>Andrew Ng updates his Machine Learning course</title><url>https://www.deeplearning.ai/program/machine-learning-specialization/</url></story><parent_chain><item><author>xwdv</author><text>Although this is the best course on ML, is it really practical for anything? Has anyone built products for things they’ve learned from this course?</text></item></parent_chain><comment><author>NelsonMinar</author><text>I thought it was useful but awfully low level. For example I hope to never, ever implement backpropagation again; I&amp;#x27;m going to use whatever code is in TensorFlow or PyTorch or whatever. But as a student I&amp;#x27;m glad I did implement it myself, once, so I understand what is going on. More broadly it demystifies the black box of machine learning methods and you can see it for the giant pile of statistical categorizing functions that it is.&lt;p&gt;The most practical takeaway I got from Ng&amp;#x27;s course was the dangers of under and overfitting your data and techniques for detecting when you make that mistake.</text></comment>
<story><title>Andrew Ng updates his Machine Learning course</title><url>https://www.deeplearning.ai/program/machine-learning-specialization/</url></story><parent_chain><item><author>xwdv</author><text>Although this is the best course on ML, is it really practical for anything? Has anyone built products for things they’ve learned from this course?</text></item></parent_chain><comment><author>MafellUser</author><text>I took this course as a defensive mechanism against BS at work, especially when the consulting Data Scientists were around. In that sense it&amp;#x27;s super practical.&lt;p&gt;ML is dominated by gigantic datasets and massive computing powers, something individuals will not have a lot of.</text></comment>