This book elaborates on different models of data science. It includes a brief description of theory, which is supported by program/code listings.
The book is composed of ten chapters. The first chapter is related to fundamental concepts in machine learning. The second chapter provides an introduction to the frameworks. The third chapter is based on linear models. The fourth chapter is on applying machine learning using survival analysis.
Chapter 5 is on nonlinear modeling. Logistic regression is also based on this method. The sixth chapter is on tree modeling; topics such as decision trees and gradient boost are covered here. In chapter 7, on neural networks, multi-layer perceptrons and deep belief networks are discussed. Chapter 8 elaborates on clustering and discusses k-means. The ninth chapter looks at principal component analysis (PCA), whereas the tenth chapter explores automated machine learning methods.
The book has a reader-centric style. Topics are covered briefly. Theoretical topics are covered only at an introductory level. The book can be considered as an introduction to various topics. Code listings and graphical results for different models are added benefits, which could enhance learning and exposure.