The chapter begins with a discussion of the principles and concepts of classification—an overview of popular classification methods, metrics, and model performance. We then explore various classification methods such as logistic regression, linear discriminant analysis (LDA), decision trees, and support vector machines. We will demonstrate these methods through Python-based tutorials. We end the chapter with a cursory glance at other classification methods—Bayes classification, Bayesian belief network, rule-based classification, K nearest neighbors, backpropagation, and genetic algorithms. Classification is further discussed in the Chapters ‘Machine Learning’ and ‘Artificial Intelligence and Deep Neural Networks’. The learnings in this chapter give a solid foundation for those discussions.