Unsupervised Machine Learning for Beginners, Part 2: Singular Value Decomposition

Last week I began this four-part series on unsupervised machine learning concepts by talking about K-means clustering. Today I want to switch gears by describing singular value decomposition unsupervised machine learning. According to Kirk Baker in his Singular Value Decomposition (SVD) Tutorial, the basic idea is'taking a high dimensional, highly variable set of data points and reducing it to a lower dimensional space; in other words, SVD can be seen as a method for data reduction.' Dimensionality reduction is usually done to get better features when you're trying to classify data for machine learning tasks.