This was a dense chapter! We introduced some of the most important concepts of ML; we know that ML has three main branches, supervised, unsupervised, and reinforcement learning, and that we will be using only supervised learning in this book. Supervised learning has two types of tasks, regression and classification, whose only difference is the type of target we want to predict. We also talked about the very abstract concepts of hypothesis set and learning algorithm, and we even invented our (very bad) pseudo-ML model.
We also talked about the very important concept of generalization, which is the whole point of building ML models: to be able to learn how to map the features to the target using the data we have, and then use this knowledge to make predictions with data that we don't have yet. Cross-validation is a set of techniques to evaluate models; the most basic...
