ABSTRACT
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 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
