Python 语言构建机器学习系统 第2版（影印版）

chinelearning
Readinginthedata
Preprocessingandcleaningthedata
Choosingtherightmodelandlearningalgorithm
Beforebuildingourfirstmodel．．．
Startingwithasimplestraightline
Towardssomeadvancedstuff
Steppingbacktogoforward-anotherlookatourdata
Trainingandtesting
Answeringourinitialquestion
Summary
Chapter2：ClassifyingwithReal-worldExamples
TheIrisdataset
Visualizationisagoodfirststep
Buildingourfirstclassificationmodel
Evaluation-holdingoutdataandcross-validation
Buildingmorecomplexclassifiers
Amorecomplexdatasetandamorecomplexclassifim
LearningabouttheSeedsdataset
Featuresandfeatureengineering
Nearestneighborclassification
Classifyingwithscikit-learn
Lookingatthedecisionboundaries
Binaryandmulticlassclassification
Summary
Chapter3：Clustering-FindingRelatedPosts
Measuringtherelatednessofposts
Hownottodoit
Howtodoit
Preprocessing-similaritymeasuredasasimilarnumberofcommonwords
Convertingrawtextintoabagofwords
Countingwords
Normalizingwordcountvectors
Removinglessimportantwords
Stemming
Stopwordsonsteroids
Ourachievementsandgoals
Clustering
K-means
Gettingtestdatatoevaluateourideason
Clusteringposts
Solvingourinitialchallenge
Anotherlookatnoise
Tweakingtheparameters
Summary
Chapter4：TopicModeling
LatentDirichletallocation
Buildingatopicmodel
Comparingdocumentsbytopics
ModelingthewholeofWikipedia
Choosingthenumberoftopics
Summary
Chapter5：Classification-DetectingPoorAnswers
Sketchingourroadmap
Learningtoclassifyclassyanswers
Tuningtheinstance
Tuningtheclassifier
Fetchingthedata
Slimmingthedatadowntochewablechunks
Preselectionandprocessingofattributes
Definingwhatisagoodanswer
Creatingourfirstclassifier
StartingwithkNN
Engineeringthefeatures
Trainingtheclassifier
Measuringtheclassifier'sperformance
Designingmorefeatures
Decidinghowtoimprove
Bias-varianceandtheirtradeoff
Fixinghighbias
Fixinghighvariance
Highbiasorlowbias
Usinglogisticregression
Abitofmathwithasmallexample
Applyinglogisticregressiontoourpostclassificationproblem
Lookingbehindaccuracy-precisionandrecall
Slimmingtheclassifier
Shipit！
Summary
Chapter6：ClassificationII-SentimentAnalysis
Sketchingourroadmap
FetchingtheTwitterdata
IntroducingtheNaiveBayesclassifier
GettingtoknowtheBayes'theorem
Beingnaive
UsingNaiveBayestoclassify
Accountingforunseenwordsandotheroddities
Accountingforarithmeticunderflows
Creatingourfirstclassifierandtuningit
Solvinganeasyproblemfirst
Usingallclasses
Tuningtheclassifier'sparameters
Cleaningtweets
Takingthewordtypesintoaccount
Determiningthewordtypes
SuccessfullycheatingusingSentiWordNet
Ourfirstestimator
Puttingeverythingtogether
Summary
Chapter7：Regression
Predictinghousepriceswithregression
Multidimensionalregression
Cross-validationforregression
Penalizedorregularizedregression
L1andL2penalties
UsingLassoorElasticNetinscikit-learn
VisualizingtheLassopath
P-greater-than-Nscenarios
Anexamplebasedontextdocuments
Settinghyperparametersinaprincipledway
Summary
Chapter8：Recommendations
Ratingpredictionsandrecommendations
Splittingintotrainingandtesting
Normalizingthetrainingdata
Aneighborhoodapproachtorecommendations
Aregressionapproachtorecommendations
Combiningmultiplemethods
Basketanalysis
Obtainingusefulpredictions
Analyzingsupermarketshoppingbaskets
Associationrulemining
Moreadvancedbasketanalysis
Summary
Chapter9：Classification-MusicGenreClassification
Sketchingourroadmap
Fetchingthemusicdata
ConvertingintoaWAVformat
Lookingatmusic
Decomposingmusicintosinewavecomponents
UsingFFTtobuildourfirstclassifier
Increasingexperimentationagility
Trainingtheclassifier
Usingaconfusionmatrixtomeasureaccuracyin
multiclassproblems
Analternativewaytomeasureclassifierperformance
usingreceiver-operatorcharacteristics
ImprovingclassificationperformancewithMel
FrequencyCepstralCoefficients
Summary
Chapter10：ComputerVision
Introducingimageprocessing
Loadinganddisplayingimages
Thresholding
Gaussianblurring
Puttingthecenterinfocus
Basicimageclassification
Computingfeaturesfromimages
Writingyourownfeatures
Usingfeaturestofindsimilarimages
Classifyingaharderdataset
Localfeaturerepresentations
Summary
Chapter11：DmensionalityReduction
Sketchingourroadmap
Selectingfeatures
Detectingredundantfeaturesusingfilters
Correlation
Mutualinformation
Askingthemodelaboutthefeaturesusingwrappers
Otherfeatureselectionmethods
Featureextraction
Aboutprincipalcomponentanalysis
SketchingPCA
ApplyingPCA
LimitationsofPCAandhowLDAcanhelp
Multidimensionalscaling
Summary
Chapter12：BiggerData
Learningaboutbigdata
Usingjugtobreakupyourpipelineintotasks
Anintroductiontotasksinjug
Lookingunderthehood
Usingjugfordataanalysis
Reusingpartialresults
UsingAmazonWebServices
Creatingyourfirstvirtualmachines
InstallingPythonpackagesonAmazonLinux
Runningjugonourcloudmachine
AutomatingthegenerationofclusterswithStarCluster
Summary
Appendix：WheretoLearnMoreMachineLearning
Onlinecourses
Books
Questionandanswersites
Blogs
Datasources
Gettingcompetitive
Allthatwasleftout
Summary
Index