Overview
Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R. The book begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to the coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with machine learning based on artificial neural networks. The first half of the book does not require mathematical sophistication, although familiarity with probability and statistics would be helpful. The second half assumes the reader is familiar with at least one semester of calculus. The text guides novice R programmers through algorithms and their application and along the way; the reader gains programming confidence in tackling advanced R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of machine learning algorithms
Full Product Details
Author: William Claster
Publisher: Taylor & Francis Ltd
Imprint: CRC Press
Weight: 0.453kg
ISBN: 9780367507855
ISBN 10: 0367507854
Pages: 408
Publication Date: 27 October 2020
Audience:
Professional and scholarly
,
General/trade
,
Professional & Vocational
,
General
Format: Paperback
Publisher's Status: Active
Availability: In Print
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Author Information
William B. Claster is a professor of mathematics and data science at Ritsumeikan Asia Pacific University in Japan, where he designed the data science curriculum and has run the data science lab since 2008. He has been recognized for his research in data science applied to the fields of medicine, social media, and geoinformatics. His research includes political analysis, stock market forecasting, tourism, and consumer behavior with machine learning applied to social media data. Originally from Philadelphia, he moved to Japan where he has been a resident there for over 20 years. In addition to research, his interests include Japanese architecture, Buddhism, and philosophy.
