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Depending on the type of tasks, there are two types of ML:

 Regression LearningIt is also called prediction model, used when the output is a numerical value that cannot be enumerated. The algorithm is requested to predict continuous results. Error metrics are used to measure the quality of the model. Example metrics are Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error.

 Classification LearningThe algorithm is asked to classify samples. It is of two subtypes: binary classification models and multiple classification models. Accuracy is used to measure the quality of a model.

The main difference between the algorithms for classification and regression is the type of output variable. Methods with quantitative outcomes are called regressions or continuous variable predictions. Methods with qualitative outputs are called classifications or discrete variable predictions.