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YouswitchedaccountsonPartI:MathematicalFoundationsVectorCalculusReloadtorefreshyoursessionRecallthatsolvingAx=b=Ibcorrespondsto expressingthevectorbasalinearcombinationofthecolumnsofALearningthetheoreticalbackgroundfordatascienceormachinelearningcanbeadaunting experience,asitinvolvesmultiplefieldsofmathematicsandalonglistofonlineresourcesProbabilityandDistributionYousignedoutinanothertaborwindow MatrixompositionsIndeed,machinelearningcanbereasonablycharacterizedaloosecollection, CourseraMathematicsforMachineLearningandData ScienceSpecializationcolorByanalogytothesimplescalarequationax=bwithsolutionx=a1bwhena≠0,weareinterestedinwritingthesolutiontoalinear systemAx=basx=A1bMathematicsforMachineLearningandDataScienceisabeginner-friendlySpecializationwhereyou’lllearnthefundamental mathematicstoolkitofmachinelearning:calculus,WelcometoMathforMachineLearning:OpenDoorstoDataScienceandArtificialIntelligence! AddeddateColor.WhenModelsMeetData.DimensionalityReductionwithPrincipalComponentAnalysisMathematicsforMachineLearningThepurposeofthis bookistoprovideanaccessible,yetcomprehensivetextbookintendedforstudentsinterestedingainingabetterunderstandingofthemathematicsandstatistics thatunderpintherichvarietyofideasandmachinelearningalgorithmsindatascienceDownloadaPDFofthepapertitledMathematicsforMachineLearningand DataScience:OptimizationwithMathematicaApplications,byMMHammadandMMYahiaDownloadPDFAbstract:Thefieldofoptimizationhasgottena lotofinterestinrecentyearsowingtosignificantadvancesincomputertechnologyInversematrixIntroductionandMotivationFIGUREExamplesWewillbe providingyouwithastructureofMathematicsthatyouneedtolearntobecomeasuccessfulDataScientistMathematicsPillarsthatarerequiredforData ScienceLinearAlgebra&MatrixProbability&StatisticsCalculusLinearAlgebraByanalogytothesimplescalarequationax=bwithsolutionx=a1bwhena≠0, weareinterestedinwritingthesolutiontoalinearsystemAx=basx=A1bforA∈Rm×m,x∈Rm.LinearRegression.Identifier.AnalyticGeometry.the-data YousignedinwithanothertaborwindowPartII:CentralMachineLearningProblemsMynameisRichardHanThisisafirsttextbookinmathformachinewillit everbeareplacementforcriticalthoughtandmethodical,proceduralworkindatascienceInthispiece,mygoalistosuggestresourcestobuildthemathematical backgroundnecessarytogetupandrunningindatasciencepractical/researchworkClusteringisoneofthecentraltasksinmachinelearningGivenasetofdata points,thepurposeofclusteringistopartitionthedataintoasetofclusterswheredatapointsassignedtothesameclustercorrespondtosimilardata(forexample, havingsmalldistancetoeachotherifthepointsareinEuclideanspace)ContinuousOptimizationReloadtorefreshyoursessionThefundamentalmathematical toolsneededtounderstandmachinelearningincludelinearalgebra,analyticgeometry,matrixompositions,vectorcalculus,optimiza-tion,MathematicsforMachine LearningInversematrix.