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What is ML? Machine Learning (ML) is defined as follows: A code learns from experience E with respect to a task T and a performance measure P, if its performance on T, as measured by P, improves with E. [1]. Example 1: your code monitors spam and classifies emails as spam or not spam (SNS). In this case, T = classifying emails as SNS; E = watching you label emails as SNS; P = the fraction of emails correctly classified as SNS. Example 2: playing checkers. E = the experience of playing many games of checkers; T = the task of playing checkers; P = the probability that the program will win the next game. ML is a part of Artificial Intelligence (AI). ML algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so [2]. ML is an important subset of data science. Through the use of statistical methods, data science algorithms are trained to make classifications or predictions, uncove