Machine Learning Without Tears, Part two: Generalization

In the first post of our non-technical ML intro series we discussed some general characteristics of ML tasks. In this post we take a first baby step towards understanding how learning algorithms work. We'll continue the dialog between an ML expert and an ML-curious person. Ok I see that an ML program can improve its performance at some task after being trained on a sufficiently large amount of data, without explicit instructions given by a human. Let's start with an extremely simple example.