Mathematics and Programming for Machine Learning with R: From the Ground Up

Based on the author's experience teaching data science for more than 10 years, Mathematics and R Programming for Machine Learning reveals how machine learning algorithms do their magic and explains how logic can be implemented in code. It is designed to give students an understanding of the logic behind machine learning algorithms as well as how to program these algorithms. Written for novice programmers, the book goes step-by-step to develop coding skills needed to implement algorithms in R.

The text begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with artificial neural network-based machine learning. The first half of the text does not require mathematical sophistication, although familiarity with probability and statistics is helpful. The second half is written for students who have taken one semester of calculus. The book guides students, who are novice R programmers, through algorithms and their application to improve the ability to code and confidence in programming R and tackling advance R programming challenges.

Highlights of the book include:

• More than 400 exercises
• A strong emphasis on improving programming skills and guiding beginners on implementing full-fledged algorithms.
• Coverage of fundamental computer and mathematical concepts including logic, sets, and probability
• In-depth explanations of the heart of AI and machine learning as well as the mechanisms that underly machine learning algorithms