There are two main techniques used in supervised learning:
Classification - A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. A classification model attempts to draw some conclusion from observed values.
Regression - A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used, the simplest is the linear regression. It tries to fit data with the best hyper-plane which goes through the points.
The difference between the two tasks is the fact that the dependent attribute is numerical for regression and categorical for classification.