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A formal definition of a ML algorithm is “A Computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks T, as measured by P, improves with experience E” [5].

 Tasks: A task defines a way to process an object or data. An example task is classification, which is a process of assigning a class label to an input object or data point. Regression is another task example, which involves assigning a real value to an object or data point.

 Performance Measure: Defines the criteria by which a ML algorithm is evaluated. In classification algorithms, accuracy refers to the percentage of correct assignment of class labels to objects or data points. Normally, data is divided into two sets. The first is used for training, while the second is used for testing.

 The Experience: It refers to the knowledge that a ML gains while learning. It divides the ML algorithms into the types explained in the next subsection.