Applied Machine Learning textcovers all the fundamentals and theoretical concepts and presents a widerange of techniques (algorithms) applicable to challenges in our day-to-daylives.

The book recognizes that most of the ideas behind machinelearning are simple and straightforward. It provides a platform for hands-onexperience through self-study machine learning projects. Datasets for somebenchmark applications have been explained to encourage the use of algorithmscovered in this book.

This is a comprehensive textbook on machine learning for undergraduates in computer science and allengineering degree programs. Post graduates and research scholars will find ita useful initial exposure to the subject, before they go for highly theoreticaldepth in the specific areas of their research. For engineers, scientists, business managers and other practitioners,the book will help build the foundations of machine learning.

CONTENT:
1. Introduction

2. Supervised Learning: Rationale and Basics

3. Statistical Learning

4. Learning With Support Vector Machines (SVM)

5. Learning With Neural Networks (NN)

6. Fuzzy Inference Systems

7. Data Clustering and Data Transformations

8. Decision Tree Learning

9. Business Intelligence and Data Mining: Techniques andApplications

? Appendix AGenetic Algorithm (GA) For Search Optimization

? Appendix BReinforcement Learning (RL)

? Datasets fromReal-Life Applications for Machine Learning Experiments