Unsupervised Machine Learning for Beginners, Part 3: Principal Component Analysis

Last week I looked at Singular Value Decomposition unsupervised machine learning technique as part of a four-part series on data science concepts for beginners. Remember that unsupervised machine learning is data driven rather than task driven (supervised machine learning). Today we'll be staying in the dimension reduction part of unsupervised machine learning as shown in the Cheat-sheet below and will talk about principal component analysis or PCA. In a similar manner to SVD, PCA is trying to reduce the number of dimensions for data exploration. The PCA method is trying to maximize variance of the data to make a predictive model and converts a set of possibly correlated variables into a set of linearly uncorrelated variables.