This chapter introduces supervised machine learning algorithms. In this chapter, the popular classification algorithms such as decision tree, random forest, knearest neighbor, Naïve Bayes classifier, and support vector machine are described in detail. Each algorithm is defined starting with its overview, followed by an algorithmic framework and a hands-on example. A detailed Python program is given at the end of each algorithm to support the precise understanding of the working behavior of the classifiers. The Python code is executed on a real dataset, which eventually gives the reader in-depth knowledge about the algorithm's applicability.