Summary
In this chapter, we introduced multiple linear regression, a generalization of simple linear regression that uses multiple features to predict the value of a response variable. We described polynomial regression, a linear model that can model non-linear relationships using polynomial feature terms. We introduced the concept of regularization, which can be used to prevent models from memorizing noise in the training data. Finally, we introduced gradient descent, a scalable learning algorithm that can estimate the parameter values that minimize a cost function.