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Linear algebra with machine learning...

Arangala, Crista.

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Linear algebra with machine learning and data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Linear algebra with machine learning and data/ Crista Arangala.
作者:	Arangala, Crista.
出版者:	Boca Raton, FL :Chapman & Hall/CRC Press, : 2023.,
面頁冊數:	1 online resource :ill.
標題:	Algebras, Linear - Textbooks. -
電子資源:	https://www.taylorfrancis.com/books/9781003025672
ISBN:	9781003025672

Linear algebra with machine learning and data
Arangala, Crista.

Linear algebra with machine learning and data[electronic resource] /Crista Arangala. - 1st ed. - Boca Raton, FL :Chapman & Hall/CRC Press,2023. - 1 online resource :ill. - Textbooks in mathematics. - Textbooks in mathematics..

Includes bibliographical references and index.

"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"--

ISBN: 9781003025672Subjects--Topical Terms:

621258
Algebras, Linear
--Textbooks.

LC Class. No.: QA184.2

Dewey Class. No.: 518/.43

Linear algebra with machine learning and data
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300 $a 1 online resource : $b ill.
490 1 $a Textbooks in mathematics
504 $a Includes bibliographical references and index.
520 $a "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"-- $c Provided by publisher.
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