book

Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

Name: Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes
ISBN: 9781484204450

by Roger Barga, Valentine Fontama, Wee Hyong Tok

November 2014

Intermediate to advanced

188 pages

4h 19m

English

Apress

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Cover
Title
Copyright
Contents at a Glance
Contents
About the Authors
Acknowledgments
Foreword
Introduction
Part 1: Introducing Data Science and Microsoft Azure Machine Learning

Chapter 1: Introduction to Data Science
What Is Data Science?Analytics SpectrumDescriptive AnalysisDiagnostic AnalysisPredictive AnalysisPrescriptive AnalysisWhy Does It Matter and Why Now?Data as a Competitive AssetIncreased Customer DemandIncreased Awareness ofData Mining TechnologiesAccess to More DataFaster and CheaperProcessing PowerThe Data Science ProcessCommon Data Science TechniquesClassification AlgorithmsClustering AlgorithmsRegression AlgorithmsSimulationContent AnalysisRecommendation EnginesCutting Edge of Data ScienceThe Rise of Ensemble ModelsSummaryBibliography
Chapter 2: Introducing Microsoft Azure Machine Learning
Hello, Machine Learning Studio!Components of an ExperimentFive Easy Steps to Creating an ExperimentStep 1: Get DataStep 2: Preprocess DataStep 3: Define FeaturesStep 4: Choose and Apply Machine Learning AlgorithmsStep 5: Predict Over New DataDeploying Your Model in ProductionDeploying Your Model into StagingTesting the Web ServiceMoving Your Model from Staging into ProductionAccessing the Azure Machine Learning Web ServiceSummary
Chapter 3: Integration with R
R in a NutshellBuilding and Deploying Your First R ScriptUsing R for Data PreprocessingUsing a Script Bundle (Zip)Building and Deploying a Decision Tree Using RSummary
Part 2: Statistical and Machine Learning Algorithms
Chapter 4: Introduction to Statistical and Machine Learning Algorithms
Regression AlgorithmsLinear RegressionNeural NetworksDecision TreesBoosted Decision TreesClassification AlgorithmsSupport Vector MachinesBayes Point MachinesClustering AlgorithmsSummary
Part 3: Practical Applications
Chapter 5: Building Customer Propensity Models
The Business ProblemData Acquisition and PreparationLoading Data from Your Local File SystemLoading Data from Other SourcesData AnalysisTraining the ModelModel Testing and ValidationModel PerformanceSummary
Chapter 6: Building Churn Models
Churn Models in a NutshellBuilding and Deploying a Customer Churn ModelPreparing and Understanding DataData Preprocessing and Feature SelectionClassification Model for Predicting ChurnEvaluating the Performance of the Customer Churn ModelsSummary
Chapter 7: Customer Segmentation Models
Customer Segmentation Models in a NutshellBuilding and Deploying Your First K-Means Clustering ModelFeature HashingIdentifying the Right FeaturesProperties of K-Means ClusteringCustomer Segmentation of Wholesale CustomersLoading the Data from the UCI Machine Learning RepositoryUsing K-Means Clustering for Wholesale Customer SegmentationCluster Assignment for New DataSummary
Chapter 8: Building Predictive Maintenance Models
OverviewThe Business ProblemData Acquisition and PreparationThe DatasetData LoadingData AnalysisTraining the ModelModel Testing and ValidationModel PerformanceModel DeploymentPublishing Your Model into StagingMoving Your Model from Staging into ProductionSummary
Index

Content preview from Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

Contents at a Glance

Part 1: Introducing Data Science and Microsoft Azure Machine Learning

Chapter 1: Introduction to Data Science

Chapter 2: Introducing Microsoft Azure Machine Learning

Chapter 3: Integration with R

Part 2: Statistical and Machine Learning Algorithms

Chapter 4: Introduction ...

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Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

by Roger Barga, Valentine Fontama, Wee Hyong Tok

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.