Machine learning is a research area where computers simulate or learn human behaviors to acquire knowledge or skills, reorganize certain information, and structure. It is an interdisciplinary field that involves computer sciences, statistics, probability theory, etc. With the continuous increase in demand for data analysis in the era of big data, how to efficiently acquire knowledge through machine learning has become one of the main tasks in research and industry. There are two machine learning approaches: supervised learning and unsupervised learning, depending on whether the data has a label or not. From the last chapter, we have identified DEGs, we are interested in identifying groups of genes that respond similarly, or which biological networks are affected. In this chapter, we will focus on unsupervised machine learning methods, including cluster analysis and dimension reduction (principal component analysis, PCA). We will learn how to cluster genes and visualize them as heatmaps and how to do PCA in R.