Logistic Regression Details Pt 2: Maximum Likelihood

This video follows from where we left off in Part 1 in this series on the details of Logistic Regression. This time we're going to talk about how the squiggly line is optimized to best fit the data.

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NOTE: In statistics, machine learning and most programming languages, the default base for the log() function is 'e'. In other words, when I write, "log()", I mean "natural log()", or "ln()". Thus, the log to the base 'e' of 2.717 = 1.

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