Machine Learning: Algorithms and Applications

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Table of Contents

Chapter 1 Introduction to Machine Learning

SECTION I SUPERVISED LEARNING ALGORITHMS
Chapter 2 Decision Trees
Chapter 3 Rule-Based Classifiers
Chapter 4 Naïve Bayesian Classification
Chapter 5 The k-Nearest Neighbors Classifiers
Chapter 6 Neural Networks
Chapter 7 Linear Discriminant Analysis
Chapter 8 Support Vector Machine

SECTION II UNSUPERVISED LEARNING ALGORITHMS
Chapter 9 k-Means Clustering
Chapter 10 Gaussian Mixture Model
Chapter 11 Hidden Markov Model
Chapter 12 Principal Component Analysis1

Appendix I:  Transcript of Conversations with Chatbot
Appendix II: Creative Chatbot