This chapter discusses dimensionality reduction. First, we will try to understand the concepts and possible uses of this unsupervised learning technique. Next, the classification of dimensionality reduction methods and popular algorithms will be reviewed. Then, two categories of dimensionality reduction methods, i.e., feature selection and feature extraction, will be introduced. In particular, two feature extraction algorithms, i.e., principal component analysis and linear discriminant analysis, will be explained in detail, considering their significance and predominance in dimensionality reduction studies. For each of them, we will try to understand the idea first, then formulate the idea using mathematical equations, and finally show how to implement the method with the equations.