Textbook

1 Introduction to Machine Learning
2 Convergence and Regression
3 Reasoning by Knowledge
4 Supervised and Unsupervised Learning
5 Reinforcement Learning
6 Association Rule Mining
7 Inductive Logic Programming
8 Clustering
9 Artificial Neural Networks
10 Deep Learning
11 Support Vector Machines
12 Ensemble Classifier
13 Fuzzy Network
15 Nearest Neighbourhood
16 Hidden Markov Models
17 Statistical Classifiers
18 Decision Trees
19 Nature Inspired Learning
Index

The present book is primarily intended for undergraduate and postgraduate students of computer science and engineering, information technology, and electrical and electronics engineering. It bridges the gaps in knowledge of the seemingly difficult areas of machine learning and nature inspired computing. The text is written in a highly interactive manner, which satisfies the learning curiosity of any reader. Content of the text has been diligently organized to offer seamless learning experience. The text begins with introduction to machine learning, which is followed by explanation of different aspects of machine learning. Various supervised, unsupervised, reinforced and nature inspired learning techniques are included in the textbook with numerous examples and case studies. Different aspects of new machine learning and nature inspired learning algorithms are explained in-depth. The well-explained algorithms and pseudocodes for each topic make this book useful for students. The book also throws light on areas like prediction and classification systems.
Day to day examples and pictorial representations for deeper understanding of the subject
Helps readers easily create programs/applications
Research oriented approach
More case studies and worked-out examples for each machine learning algorithm than any other book