A Guide to Principal Component Analysis (PCA) for Machine Learning
Principal Component Analysis (PCA) is one of the most commonly used unsupervised machine learning algorithms across a variety of applications: exploratory data analysis, dimensionality reduction, information compression, data de-noising, and plenty more. In this blog, we will go step-by-step and cover: Before we delve into its inner workings, let’s first get a better understanding of PCA. Imagine we have a 2-dimensional dataset.