Dimensionality reduction is a common step in machine learning, and Principal Component Analysis (PCA) is a popular technique for this. PCA helps find the main directions of variation in data. The article explains the basics of PCA and two methods to calculate it. It also includes examples of applying PCA in biometrics, image compression, and visualizing complex datasets.