If you are reading this book, you probably already know what supervised and unsupervised learning are, but for the sake of completion, let's briefly summarize what they mean. In supervised learning, we train the algorithms with labeled data. Labeled data is nothing but input data along with the outcome variable. For example, if our intention is to predict whether a website is about news, we would be preparing a sample dataset of website content with "news" and "not news" as labels. This dataset is called the training dataset.

With supervised learning, our end goal is to use the training dataset and come up with a function that maps our input variables to an output variable with least margin of error. We call input variables (or x variables) features or explanatory variables, and the output variable (also known as the y variable or label) the target or dependent variable. In the news website example, the text content in the website would be the input variable and "news" or "not news" would be the target variable. The function, along with its parameters (or weights or theta), is our hypothesis, or model.

In the case of unsupervised learning, we aim to find a structure within the data—groups and relationships among these groups or the participants of a group. Unlike supervised learning, we don't know any information about the data or even its subset. An example would be to see whether there are similar buying patterns among a group of people (which helps cross-selling) or to see which group of people is more likely to buy pizza from our newly opened store.