Brilliant explanation of Bayesian Probability

Bayes' theorem of probability plays a major role in artificial intelligence and machine learning. The core of the theorem is that we can make predictions about the probability of something happening based on prior knowledge of variables that might be related to that something happening.

The (simple) statement of the equation looks like this (the full proof is here):

The gist behind Bayes' theorem is easy enough to wrap your mind around, but if you want to start applying Bayesian probability in the context of machine learning and, like me, you didn't study statistics, I'd highly recommend this frankly brilliant explanation of the rule.