<story><title>Introduction to Data-Centric AI</title><url>https://dcai.csail.mit.edu/</url></story><parent_chain></parent_chain><comment><author>yeldarb</author><text>Love it; one of the biggest things we&amp;#x27;ve preached to users of our computer vision dev-tool is: it doesn&amp;#x27;t matter how much time you spend on your model if your data is bad.&lt;p&gt;When you&amp;#x27;re trying to get a first version of your CV project launched, you can usually pretty much ignore the model (and pick one up off the shelf) because the time you spend on improving your data is orders of magnitude higher leverage.&lt;p&gt;Someday the model might become the limiting re-agent but it&amp;#x27;s much, much later than people assume (if at all). But the model is the &amp;quot;fun&amp;quot; part for technical people to play with (and academia encourages treating your data as an immutable constant) so it gets a lot of attention at the expense of the data.</text></comment>
<story><title>Introduction to Data-Centric AI</title><url>https://dcai.csail.mit.edu/</url></story><parent_chain></parent_chain><comment><author>doctoboggan</author><text>&amp;gt; Anyone is welcome to take this course, regardless of background. To get the most out of this course, we recommend that you:&lt;p&gt;&amp;gt; Completed an introductory course in machine learning (like 6.036 &amp;#x2F; 6.390). To learn this on your own, check out Andrew Ng’s ML course, fast.ai’s ML course, or Dive into Deep Learning.&lt;p&gt;Does anyone have a recommendation for one of these options over the others?</text></comment>