Linear Algebra with Machine Learning and Data
Linear Algebra with Machine Learning and Data by Crista Arangala, published by CRC Press in 2023, is a comprehensive resource that explores key linear algebra concepts in the context of data analytics and data mining. Spanning 290 pages, this edition is designed for students who have a foundational understanding of linear algebra and wish to apply these principles to real-world scenarios. The book employs a case study approach, grounding each topic in practical applications to enhance understanding.
Readers will find a structured exploration of linear algebra’s relevance to data analytics, including a supplemental chapter on Decision Trees and their role in regression analysis. The text covers essential topics such as clustering and interpolation, providing valuable insights for students in undergraduate mathematics. Each chapter includes real-world data sets and offers links to Github documentation for Python and R syntax, along with exercises that encourage the application of learned concepts. An overview of key concepts is also provided to support readers in their studies.
Official synopsis Publisher
This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application.
This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis. The text can be considered in two different but overlapping general data analytics categories, clustering and interpolation.
Knowledge of mathematical techniques related to data analytics, and exposure to interpretation of results within a data analytics context, are particularly valuable for students studying undergraduate mathematics. Each chapter of this text takes the reader through several relevant and case studies using real world data.
All data sets, as well as Python and R syntax are provided to the reader through links to Github documentation. Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics.
A basic knowledge of the concepts in a first Linear Algebra course are assumed; however, an overview of key concepts are presented in the Introduction and as needed throughout the text.
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