# Mathematics and Programming for Machine Learning with R

PDF, ePUB

- eBook:Mathematics and Programming for Machine Learning with R: From the Ground Up
- Author:William B. Claster
- Edition:1 edition
- Categories:
- Data:October 27, 2020
- ISBN:0367561948
- ISBN-13:9780367561949
- Language:English
- Pages:430 pages
- Format:PDF, ePUB

**Book Description**

*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

**Content**

2. Logic and R

3. Sets with R: Building the Tools

4. Probability

5. Naïve Rule

6. Complete Bayes

7. Naïve Bayes Classifier

8. Stored Model for Naïve Bayes Classifier

9. Review of Mathematics for Neural Networks

10. Calculus

11. Neural Networks – Feedforward Process and Backpropagation Process

12. Programming a Neural Network Using OOP in R

13. Adding in a Bias Term

14. Modular Version of Neural Networks for Deep Learning

15. Deep Learning with Convolutional Neural Networks

16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes

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