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.
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.