Mathematics for Machine Learning: Math for Aspiring Data Scientists

As you might imagine, it is very rare for our students to provide feedback along the lines of, “why so much programming?” Some students comment that there was more programming than they expected, but rarely is the need for a Data Scientist to have coding skills questioned..Not so for mathematics..This is unfortunate as I would strenuously argue that without some mathematical knowledge a Data Scientist will not be able to build effective models..A Hypothetical Example Here’s a hypothetical example to illustrate my point..An aspiring Data Scientist does some research regarding a particular problem and finds a blog post, a paper, and/or a forum post recommending the application of a regression model built with Stochastic Gradient Descent to the problem space. The following screenshot is an excerpt from Python’s most excellent scikit-learn library..NOTE – Rest assured that similar R examples exist as well (e.g., the awesome glmnet pacakge) and I only use scikit-learn here as the scikit-learn HTML documentation is more visually attractive ;-)..The above green boxes illustrate some of the mathematical knowledge required to use this algorithm to build the most effective model..For example: The Stochastic Gradient Descent algorithm – what is it and how does it work..Regularization – what is it and how does it work..The differences between L1 and L2 regularization – why a Data Scientist might want one vs..the other or a blend of both..I believe this relatively simple example illustrates my point about math and programming.. More details

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