Mathematical Background for Predictive Analytics

In this chapter, we present the mathematical foundations required by data scientists to perform predictive analytics. The topics include basics concepts of linear algebra such as introduction to vectors, matrices determinants, and equations for simple linear regression (SLR); dimensionality reduction techniques including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) which are vital preprocessing steps to perform predictive analytics; and mathematical foundations for neural networks that will lay the foundations for the deep learning architectures discussed in the latter chapters. Here, examples are provided along the way to illustrate the mathematical concepts and lay the foundations for future topics.