Machine Learning / Saikat Dutt, Subramanian Chandramouli and Amit Kumar Das

It includes Appendix and index pages

Machine Learning introduces readers to the area of machine learning in an extremely easy to read and understand manner. Through numerous worked-out problems, diagrams and notes, the text makes this challenging subject easy to assimilate. The text starts with a simple introduction to the concepts of machine learning and expands it by delving into the details of different learning algorithms by using sample caselets. The authors, by virtue of their long exposure to industry implementations, have designed the topics so that readers will earn industry-readiness by just reading this text.

Table of Content

1 Introduction to Machine Learning
2 Preparing to Model
3 Modelling and Evaluation
4 Basics of Feature Engineering
5 Brief Overview of Probability
6 B ayesian Concept Learning
7 Super vised Learning: Classification
8 Super vised Learning: Regression
9 Unsupervised Learning
10 Basics of Neural Network
11 Other Types of Learning"

Salient Features

1. Hands-on implementation of machine learning in R and Python
2. In-depth treatment of supervised and unsupervised learning
3. Examples that showcase the use of machine learning in the industry
4. 400+ sample questions and 3 full-length sample exam papers