Introduction

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book gives an overview of background expectations. It focuses on the implementation of Singular Value Decomposition and Principal Component Analysis in the context of data science. The book focus on predictive models. It focuses more on traditional interpolation techniques such as Chebyshev, Hermite, and Lagrange interpolation. The book takes a quick look at probability theory and matrix calculus before connecting the material. It focuses on implementing the concepts of previous chapters in decision trees. The book focuses on many models built around random matrices. It relies heavily on probability theory.