There is no doubt about a big impact to different industries by AIArtificial Intelligence (AI) revolution. AIArtificial Intelligence (AI) improves efficiency and decision-making in various industries such as healthcare, food, public safety, manufacturing, automotives, and so on. AIArtificial Intelligence (AI) is a multidisciplinary research field covering computer science, mathematics, linguistics, biology, electrical engineering, mechanical engineering, social sciences and so on. Good combination of them is essential to create efficient AIArtificial Intelligence (AI) systems. Depending on AIArtificial Intelligence (AI) applications and use cases, their combination would be different. However, the core of AIArtificial Intelligence (AI) applications is how to design AIArtificial Intelligence (AI) algorithms for them. There are multiple AIArtificial Intelligence (AI) algorithm categories falling into supervised learningSupervised learning, unsupervised learningUnsupervised learning, reinforcement learningReinforcement learning, deep learning, natural language processingNatural language processing, distributed learning, and so on. In this chapter, we discuss how they work, and which problems are better suited for AIArtificial Intelligence (AI) algorithms.