Linear Algebra With Machine Learning and Data

This book is a comprehensive guide to linear algebra for data analytics and data mining, covering key topics such as clustering, interpolation, and decision trees. It uses a case study approach with real-world applications and provides links to Github documentation for further exploration.

Format: Hardback
Length: 290 pages
Publication date: 30 March 2023
Publisher: Taylor & Francis Ltd

This comprehensive book delves into various essential linear algebra topics, specifically tailored for applications in data analytics and data mining. It adopts a case study approach, grounding each case in real-world scenarios. The text serves as a valuable resource for a second course in linear algebra for data analytics, with an additional chapter dedicated to decision trees and their applications in regression analysis. It can be broadly categorized into two main areas: clustering and interpolation.

For students pursuing undergraduate mathematics, a strong foundation in mathematical techniques related to data analytics and the ability to interpret results within a data analytics context are highly valuable. Each chapter of this text presents several relevant case studies utilizing real-world data.

To facilitate learning, all data sets, along with Python and R syntax, are conveniently provided through links to Github documentation. Following each chapter, a series of short exercise sets encourage students to apply their growing knowledge of linear algebra in data analytics using technology.

While a basic understanding of concepts from a first linear algebra course is assumed, an overview of key concepts is presented in the Introduction and as needed throughout the text. This book offers a comprehensive and practical guide for students and professionals seeking to leverage linear algebra in the field of data analytics.

Dimension: 234 x 156 (mm)
ISBN-13: 9780367458393