精通Java机器学习
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￥ 37.8 3.5折 ￥ 108 全新
库存4件
作者Uday,Kamath 著
出版社东南大学出版社
ISBN9787564178642
出版时间2018-10
版次1
装帧平装
开本16开
纸张胶版纸
页数519页
字数99999千字
定价108元
货号7919797
上书时间2024-06-25
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基本信息
书名:精通Java机器学习
定价：108元
作者:Uday,Kamath 著
出版社：东南大学出版社
出版日期：2018-10-01
ISBN：9787564178642
字数：681000
页码：519
版次：1
装帧：平装
开本：16开
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内容提要
目录
PrefaceChapter 1: Machine Learning ReviewMachine learning - history and definitionWhat is not machine learning?Machine learning - concepts and terminologyMachine learning - types and subtypesDatasets used imachine learningMachine learning applicationsPractical issues imachine learningMachine learning - roles and processRolesProcessMachine learning -tools and datasetsDatasetsSummaryChapter 2: Practical Approach to Real-World Supervised LearningFormal descriptioand notationData quality analysisDescriptive data analysisBasic label analysisBasic feature analysisVisualizatioanalysisUnivariate feature analysisMultivariate feature analysisData transformatioand preprocessingFeature constructionHandling missing valuesOutliersDiscretizationData samplingIs sampling needed?Undersampling and oversamplingTraining, validation, and test setFeature relevance analysis and dimensionality reductionFeature search techniquesFeature evaluatiotechniquesFilter approachWrapper approachEmbedded approachModel buildingLinear modelsLinear RegressionNaive BayesLogistic RegressionNon-linear modelsDecisioTreesK-Nearest Neiors (KNN)Support vector machines (SVM)Ensemble learning and meta learnersBootstrap aggregating or baggingBoostingModel assessment, evaluation, and comparisonsModel assessmentModel evaluatiometricsConfusiomatrix and related metricsROC and PRC curvesGaicharts and lift curvesModel comparisonsComparing two algorithmsComparing multiple algorithmsCase Study - Horse Colic ClassificationBusiness problemMachine learning mappingData analysisLabel analysisFeatures analysisSupervised learning experimentsWeka experimentsRapidMiner experimentsResults, observations, and analysisSummaryReferencesChapter 3: Unsupervised Machine Learninq Techniques……Chapter 4: Semi-Supervised and Active LearningChapter 5: Real-Time Stream Machine LearningChapter 6: Probabilistic Graph ModelingChapter 7: Deep LearningChapter 8: Text Mining and Natural Language ProcessingChapter 9: Bia Data Machine Learnina - The Final FrontierAppendix A: Linear AlgebraAppendix B: ProbabilityIndex
作者介绍
乌代？卡马特(Dr. Uday Kamath) is the chief data scientist at BAE Systems Applied Intelligence. He speizes in scalable machine learning and has spent 20 years in the domain of AML, fraud detection in finan crime, cyber security, and bioinformatics, to name a few. Dr. Kamath is responsible for key products in areas focusing on the behavioral, so networking and big data machine learning aspects of analytics at BAE AI. He received his PhD at George Mason University, under the able guidance of Dr. Kenneth De Jong, where his dissertation research focused on machine learning for big data and automated sequence mining.
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