An important subfield of machine learning is supervised learning. In supervised learning, we try to learn from a set of labeled training data; that is, every data sample has a desired target value or true output value. These target values could correspond to the continuous output of a function (such as y in y = sin(x)), or to more abstract and discrete categories (such as cat or dog). If we are dealing with continuous output, the process is called regression, and if we are dealing with discrete output, the process is called classification. Predicting housing prices from sizes of houses is an example of regression. Predicting the species from the color of a fish would be classification. In this chapter, we will focus on classification using SVMs.