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Full Description
This textbook provides semester-length coverage of pattern recognition/classification, accessible to everyone who would like to understand how pattern recognition and machine learning works. It explores the most commonly used classification methods in an intelligible way. Unlike other books available for this course, this one explains from top to bottom each method with all needed details. Every method described is explained with examples in Python. The presentation is designed to be highly accessible to students from a variety of disciplines, with no experience in machine learning. Each chapter contains easy to understand code samples, as well as exercises to consolidate and test knowledge.
Contents
A Gentle Introduction.- Classifiers based on probability theory.- Unsupervised methods.- Decision trees.- Neural networks.- Support Vector Machine.- Ensemble methods.- Deep Learning.
