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.

chapter Chapter 1|34 pages

Introduction to Machine Learning

part Section 1|94 pages

Supervised Learning Algorithms

chapter Chapter 2|16 pages

Decision Trees

chapter Chapter 3|20 pages

Rule-Based Classifiers

chapter Chapter 4|10 pages

Naïve Bayesian Classification

chapter Chapter 5|6 pages

The k-Nearest Neighbors Classifiers

chapter Chapter 6|18 pages

Neural Networks

chapter Chapter 7|8 pages

Linear Discriminant Analysis

chapter Chapter 8|14 pages

Support Vector Machine

part Section 2|60 pages

Unsupervised Learning Algorithms

chapter Chapter 9|6 pages

k-Means Clustering

chapter Chapter 10|8 pages

Gaussian Mixture Model

chapter Chapter 11|8 pages

Hidden Markov Model

chapter Chapter 12|36 pages

Principal Component Analysis