Machine Learning: Algorithms and Applications

  • 2h 6m
  • Eihab Bashier Mohammed Bashier, Mohssen Mohammed, Muhammad Badruddin Khan
  • CRC Press
  • 2017

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.

In this Book

  • Introduction
  • Introduction to Machine Learning
  • Decision Trees
  • Rule-Based Classifiers
  • Naïve Bayesian Classification
  • The k-Nearest Neighbors Classifiers
  • Neural Networks
  • Linear Discriminant Analysis
  • Support Vector Machine
  • k-Means Clustering
  • Gaussian Mixture Model
  • Hidden Markov Model
  • Principal Component Analysis
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