Classification and clustering algorithms of machine learning with their applications

Abstract

In order to minimize human effort and increase efficiency, we use machines. But nowadays, advancements have been done to such an extent that machines can learn from experience and make decisions by itself substituting humans. Machine learning is basically a subfield of Artificial Intelligence, which is based on the principal of a machine being able to analyze patterns, learn from data and thereby make decisions itself with minimal or none explicit assistance. This is an introductory chapter to machine learning containing supervised, unsupervised, semi-supervised, and reinforcement algorithms and applications of machine learning. This chapter covered four classification techniques (Logistic Regression, Decision Tree, K-Nearest Neighbors, and Naive Bayes) and K means, and Hierarchical clustering algorithms considering two well-known datasets (Iris and tennis) using Python.