Currently, most of the machine learning products use supervised learning. In this, we have a set of features or inputs X (for example, an image) and our model will predict a target or output variable y (for example, caption for the image).
In other words, our model learns a function that maps inputs to desired outputs. Features are independent variables and targets are the dependent variable.
Supervised learning problems can be further grouped into classification and regression problems. When the output variable is a category, such as "spam" or "ham" (non-spam) then the problem is a classification problem. When the output variable is a real value, such as "price of the house", then it is a regression problem.
- Spam filtering: Is an email spam or not
- Image classification: Given an image, output which objects are present in the image (dog, cat, computer, building, so on)
- Given information about a house, predict its price
- Netflix: Given a user and a movie, predict the rating the user is going to give to the movie (which can then be used for providing recommendations)