04456cam a22005891i 4500 9781003051220 FlBoTFG 20260210180857.0 m d cr ||||||||||| 200818t20202021flua ob 000 0 eng d OCoLC-P eng rda pn OCoLC-P 9781000196993 (ePub ebook) 1000196992 (ePub ebook) 9781000196979 (PDF ebook) 1000196976 (PDF ebook) 9781000196986 (Mobipocket ebook) 1000196984 (Mobipocket ebook) 9781003051220 (ebook) 1003051227 (ebook) 9780367561949 (hbk.) 9780367507855 (pbk.) 10.1201/9781003051220 doi (OCoLC)1198598560 (OCoLC-P)1198598560 Q325.5 .C53 2020eb COM 004000 bisacsh COM 051300 bisacsh COM 051210 bisacsh UY bicssc 006.31 23 Claster, William B., author. Mathematics and R programming for machine learning : from the ground up / William B. Claster. Boca Raton, FL : CRC Press, 2020. ©2021 1 online resource : illustrations (black and white) text rdacontent still image rdacontent computer rdamedia online resource rdacarrier <P>Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Naïve Rule. Chapter 6. Complete Bayes. Chapter 7. Naïve Bayes Classifier. Chapter 8. Stored Model for Naive Bayes Classifier. Chapter 9. Review of Mathematics for Neural Networks. Chapter 10. Calculus. Chapter 11. Neural Networks -- Feed Forward Process and Back Propagation Process. Chapter 12. Programming a Neural Network using OOP in R. Chapter 13. Adding in a Bias Term. Chapter 14. Modular Version of Neural Networks for Deep Learning. Chapter 15. Deep Learning with Convolutional Neural Networks. Chapter 16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes.</P> 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 OCLC-licensed vendor bibliographic record. Machine learning. 1623 R (Computer program language) Programming (Mathematics) COMPUTERS / Artificial Intelligence bisacsh COMPUTERS / Programming / Algorithms bisacsh 4941 COMPUTERS / Programming / Object Oriented bisacsh Taylor & Francis https://www.taylorfrancis.com/books/9781003051220 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 93261 93260