This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application. This book dives into how geometry, network science, and topology drive machine-learning algorithms--and how you can use them in all kinds of data analysis. Through vivid case studies in the R programming language, and detailed illustrations of major concepts, you'll learn practical scientific applications for these algorithms across many types of data and fields of study. There are also plenty of hands-on examples of relevant code covered throughout, which you'll use for your own natural language processing project in one of the book's final chapters.
