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Python 语言构建机器学习系统 第2版(影印版)

Python 语言构建机器学习系统 第2版(影印版)-电子书下载


作者: Luis·Pedro·Coelho/WilliRi·chert
出版社: 东南大学出版社
原作名: Building Machine Learning Systems With Python Second Edition
出版年: 2016-1-1
页数: 301
定价: 68.00元
装帧: 平装
丛书: Packt Publishing 影印版丛书
ISBN: 9787564160623

下载次数:19


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  • 内容简介
  • 作者介绍
  • 书籍目录

运用机器学习获得对于数据的深入洞见,是现代应用开发者和分析师的关键技能。Python是一种可以用于开发机器学习应用的语言。作为一种动态语言,它可以进行快速探索和实验。利用其的开源机器学习库,你可以在快速尝试很多想法的同时专注于手头的任务。
《Python语言构建机器学习系统(第2版 影印版 英文版)》展示了如何在原始数据中寻找模式的具体方法,从复习Python机器学习知识和介绍程序库开始,你将很快进入应对正式而真实的数据集项目环节,运用建模技术,创建推荐系统。然后,《Python语言构建机器学习系统(第2版 影印版 英文版)》介绍了主题建模、篮子分析和云计算等高级主题。这些内容将拓展你的能力,让你能够创建大型复杂系统。
有了《Python语言构建机器学习系统(第2版 影印版 英文版)》,你就能获得构建自有系统所需的工具和知识,定制化解决实际的数据分析相关问题。

运用机器学习获得对于数据的深入洞见,是现代应用开发者和分析师的关键技能。Python是一种可以用于开发机器学习应用的语言。作为一种动态语言,它可以进行快速探索和实验。利用其的开源机器学习库,你可以在快速尝试很多想法的同时专注于手头的任务。
《Python语言构建机器学习系统(第2版 影印版 英文版)》展示了如何在原始数据中寻找模式的具体方法,从复习Python机器学习知识和介绍程序库开始,你将很快进入应对正式而真实的数据集项目环节,运用建模技术,创建推荐系统。然后,《Python语言构建机器学习系统(第2版 影印版 英文版)》介绍了主题建模、篮子分析和云计算等高级主题。这些内容将拓展你的能力,让你能够创建大型复杂系统。
有了《Python语言构建机器学习系统(第2版 影印版 英文版)》,你就能获得构建自有系统所需的工具和知识,定制化解决实际的数据分析相关问题。

Preface
Chapter1:GettingStartedwithPythonMachineLearning
MachinelearningandPython-adreamteam
Whatthebookwillteachyou(andwhatitwillnot)
Whattodowhenyouarestuck
Gettingstarted
IntroductiontoNumPy,SciPy,andmatplotlib
InstallingPython
ChewingdataefficientlywithNumPyandintelligentlywithSciPy
LearningNumPy
Indexing
Handlingnonexistingvalues
Comparingtheruntime
LearningSciPy
Ourfirst(tiny)applicationofm

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