Machine Learning is sometimes confusing. From the esoteric terms to elevated expositions it seems like a terribly difficult area to get into. Since I started as a developer I totally get the mismatch! In this episode we tackle the one term that is used all of the time in Machine Learning: the elusive "model." First we set up how machine learning is different, how to think about it, and finally what a model actually is (spoiler alert - think "a function written a different way"). Would love your feedback!