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首页R Machine Learning By Example
R Machine Learning By Example Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to make machine learning give them datadriven insights to grow their businesses. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.
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TableofContents
RMachineLearningByExample
Credits
AbouttheAuthors
AbouttheReviewer
www.PacktPub.com
eBooks,discountoffers,andmore
Whysubscribe?
Preface
Whatthisbookcovers
Whatyouneedforthisbook
Whothisbookisfor
Conventions
Readerfeedback
Customersupport
Downloadingtheexamplecode
Downloadingthecolorimagesofthisbook
Errata
Piracy
Questions
1.GettingStartedwithRandMachineLearning
DelvingintothebasicsofR
UsingRasascientificcalculator
Operatingonvectors
Specialvalues
DatastructuresinR
Vectors
Creatingvectors
Indexingandnamingvectors
Arraysandmatrices
Creatingarraysandmatrices
Namesanddimensions
Matrixoperations
Lists
Creatingandindexinglists
Combiningandconvertinglists

Dataframes
Creatingdataframes
Operatingondataframes
Workingwithfunctions
Built-infunctions
User-definedfunctions
Passingfunctionsasarguments
Controllingcodeflow
Workingwithif,if-else,andifelse
Workingwithswitch
Loops
Advancedconstructs
lapplyandsapply
apply
tapply
mapply
NextstepswithR
Gettinghelp
Handlingpackages
Machinelearningbasics
Machinelearning–whatdoesitreallymean?
Machinelearning–howisitusedintheworld?
Typesofmachinelearningalgorithms
Supervisedmachinelearningalgorithms
Unsupervisedmachinelearningalgorithms
PopularmachinelearningpackagesinR
Summary
2.Let'sHelpMachinesLearn
Understandingmachinelearning
Algorithmsinmachinelearning
Perceptron
Familiesofalgorithms
Supervisedlearningalgorithms
Linearregression
K-NearestNeighbors(KNN)
Collectingandexploringdata
Normalizingdata
Creatingtrainingandtestdatasets
Learningfromdata/trainingthemodel

Evaluatingthemodel
Unsupervisedlearningalgorithms
Apriorialgorithm
K-Means
Summary
3.PredictingCustomerShoppingTrendswithMarketBasketAnalysis
Detectingandpredictingtrends
Marketbasketanalysis
Whatdoesmarketbasketanalysisactuallymean?
Coreconceptsanddefinitions
Techniquesusedforanalysis
Makingdatadrivendecisions
Evaluatingaproductcontingencymatrix
Gettingthedata
Analyzingandvisualizingthedata
Globalrecommendations
Advancedcontingencymatrices
Frequentitemsetgeneration
Gettingstarted
Dataretrievalandtransformation
Buildinganitemsetassociationmatrix
Creatingafrequentitemsetsgenerationworkflow
Detectingshoppingtrends
Associationrulemining
Loadingdependenciesanddata
Exploratoryanalysis
Detectingandpredictingshoppingtrends
Visualizingassociationrules
Summary
4.BuildingaProductRecommendationSystem
Understandingrecommendationsystems
Issueswithrecommendationsystems
Collaborativefilters
Coreconceptsanddefinitions
Thecollaborativefilteringalgorithm
Predictions
Recommendations
Similarity
Buildingarecommenderengine

Matrixfactorization
Implementation
Resultinterpretation
Productionreadyrecommenderengines
Extract,transform,andanalyze
Modelpreparationandprediction
Modelevaluation
Summary
5.CreditRiskDetectionandPrediction–DescriptiveAnalytics
Typesofanalytics
Ournextchallenge
Whatiscreditrisk?
Gettingthedata
Datapreprocessing
Dealingwithmissingvalues
Datatypeconversions
Dataanalysisandtransformation
Buildinganalysisutilities
Analyzingthedataset
Savingthetransformeddataset
Nextsteps
Featuresets
Machinelearningalgorithms
Summary
6.CreditRiskDetectionandPrediction–PredictiveAnalytics
Predictiveanalytics
Howtopredictcreditrisk
Importantconceptsinpredictivemodeling
Preparingthedata
Buildingpredictivemodels
Evaluatingpredictivemodels
Gettingthedata
Datapreprocessing
Featureselection
Modelingusinglogisticregression
Modelingusingsupportvectormachines
Modelingusingdecisiontrees
Modelingusingrandomforests
Modelingusingneuralnetworks
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janne2008
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