Supervised machine learning algorithms are designed to learn by example. The name “supervised” learning originates from the idea that training this type of algorithm is like having a teacher supervise the whole process.
1) Classification : It is a Supervised Learning task where output is having defined labels(discrete value). The goal here is to predict discrete values belonging to a particular class and evaluate on the basis of accuracy.
It can be either binary or multi class classification. In binary classification, model predicts either 0 or 1 ; yes or no but in case of multi class classification, model predicts more than one class.
For classification models see here
2) Regression : It is a Supervised Learning task where output is having continuous value. The goal here is to predict a value as much closer to actual output value as our model can and then evaluation is done by calculating error value. The smaller the error the greater the accuracy of our regression model.
For regression models see here