Supervised learning

Supervised learning involves training a model on a dataset that contains both input features and the corresponding correct outputs (labels).  The goal is to predict the output for new, unseen inputs based on the learned function. 
There are two main types of supervised learning:

• Classification: the goal is to predict a class label, which is a choice from a predefined list of possibilities. For example, imagine trying to sort emails into "spam" or "not spam." Or when your phone recognizes your face to unlock, it's using classification to say, "Yep, that's you!"
• Regression: the goal is to predict a quantity. For example, predicting the price of a house based on various features like its size and location, or figuring out how much a car is worth based on its make, model, and age. It's like playing "The Price is Right."

As we go through different algorithms in this course, we'll talk about whether they're more like supervised or unsupervised learning.