03283cam a2200529 i 45000010014000000030008000140050017000220060019000390070015000580080041000730400035001140200036001490200033001850200042002180200039002600200018002990200018003170200043003350200040003780240031004180350022004490350024004710500012004950720025005070720025005320720016005571000038005732450070006112500019006812640036007003000039007363360026007753370026008013380036008274900029008635201377008925880047022696500051023166500042023676500040024096500053024496500048025028560072025508560102026229420012027249990017027369781003025672FlBoTFG20251030122226.0m     o  d        cr cnu|||unuuu230509s2023    flua    ob    001 0 eng d  aOCoLC-PbengerdaepncOCoLC-P  a9781003025672q(electronic bk.)  a1003025676q(electronic bk.)  a9781000856163q(electronic bk. : PDF)  a100085616Xq(electronic bk. : PDF)  z9780367458393  z9781032458649  a9781000856200q(electronic bk. : EPUB)  a1000856208q(electronic bk. : EPUB)7 a10.1201/97810030256722doi  a(OCoLC)1378643711  a(OCoLC-P)1378643711 4aQA184.2 7aMATx0020502bisacsh 7aMATx0030002bisacsh 7aPBF2bicssc1 aArangala, Crista,eauthor.99258910aLinear algebra with machine learning and data /cCrista Arangala.  aFirst edition. 1aBoca Raton :bCRC Press,c2023.  a1 online resource :billustrations  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier0 aTextbooks in mathematics  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"--cProvided by publisher.  aOCLC-licensed vendor bibliographic record. 7aMATHEMATICS / Algebra / Linear2bisacsh992590 7aMATHEMATICS / Applied2bisacsh963964 0aAlgebras, LinearvTextbooks.965435 0aMachine learningxMathematicsvTextbooks.992591 0aData miningxMathematicsvTextbooks.992592403Taylor & Francisuhttps://www.taylorfrancis.com/books/9781003025672423OCLC metadata license agreementuhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf  cE-Books  c67350d67350