Machine Learning: Algorithms and Applications

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Mohssen Mohammed (Al-Imam Muhammad Ibn Saud University, Saudi Arabia) Muhammad Badruddin Khan (Imam Muhammad Ibn Saud University, Riyadh, Saudi Arabia)

Machine Learning

Algorithms and Applications

Mohssen Mohammed (Al-Imam Muhammad Ibn Saud University, Saudi Arabia), Muhammad Badruddin Khan (Imam Muhammad Ibn Saud University, Riyadh, Saudi Arabia), Eihab Bashier Mohammed Bashier (Faculty of Sciences and Arts in Baljurashi at Albaha University, Saudi Arabia)

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English

CRC Press
30 June 2020

Automatic control engineering; Algorithms & data structures; Machine learning

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.

By: Mohssen Mohammed (Al-Imam Muhammad Ibn Saud University Saudi Arabia), Muhammad Badruddin Khan (Imam Muhammad Ibn Saud University, Riyadh, Saudi Arabia), Eihab Bashier Mohammed Bashier (Faculty of Sciences and Arts in Baljurashi at Albaha University, Saudi Arabia)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9780367574673
ISBN 10: 0367574675
Pages: 226
Publication Date: 30 June 2020
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

Exact String Matching Algorithms. Brute-Force Algorithm. Aho-Corasick Algorithm. Boyer-Moore Algorithm. Rabin–Karp Algorithm. Knuth–Morris–Pratt Algorithm. Approximate String Matching Algorithms. Longest Common Subsequence. Longest Increasing Subsequence. Longest Common Substring. Supervised Learning. Decision Trees. C4.5 Algorithm. RIPPER Algorithm. Perceptron-Based Techniques. Naive Bayes Classifiers. Bayesian Networks. Instance-Based Learning. Support Vector Machines. Unsupervised Learning. Bayes Rule. Factor Analysis. Principal Components Analysis (PCA). Independent Components Analysis (ICA). Mixture of Gaussians. K-Means. Expectation-Maximization (EM) Algorithm. State-Space Models (Ssms). Hidden Markov Models (Hmms). Undirected Graphs. Directed Graphs. Factor Graphs.

Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier