book

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition

Name: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition
ISBN: 9781484212004

by Roger Barga, Valentine Fontama, Wee Hyong Tok

August 2015

Intermediate to advanced

320 pages

7h 25m

English

Apress

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Cover
Title
Copyright
Contents at a Glance
Contents
About the Authors
About the Techincal reviewers
Foreword
Acknowledgments
Introduction

Part I: 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 of Data Mining TechnologiesAccess to More DataFaster and Cheaper Processing 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 ExperimentIntroducing the GalleryFive Easy Steps to Creating a Training ExperimentStep 1: Getting the DataStep 2: Preprocessing the DataStep 3: Defining the FeaturesStep 4: Choosing and Applying Machine Learning AlgorithmsStep 5: Predicting Over New DataDeploying Your Model in ProductionCreating a Predictive ExperimentPublishing Your Experiment as a Web ServiceAccessing the Azure Machine Learning Web ServiceSummary
Chapter 3: Data Preparation
Data Cleaning and ProcessingGetting to Know Your DataMissing and Null ValuesHandling Duplicate RecordsIdentifying and Removing OutliersFeature NormalizationDealing with Class ImbalanceFeature SelectionFeature EngineeringBinning DataThe Curse of DimensionalitySummary
Chapter 4: 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
Chapter 5: Integration with Python
OverviewPython JumpstartUsing Python in Azure ML ExperimentsUsing Python for Data PreprocessingCombining Data using PythonHandling Missing Data Using PythonFeature Selection Using PythonRunning Python Code in an Azure ML ExperimentSummary
Part II: Statistical and Machine Learning Algorithms
Chapter 6: Introduction to Statistical and Machine Learning Algorithms
Regression AlgorithmsLinear RegressionNeural NetworksDecision TreesBoosted Decision TreesClassification AlgorithmsSupport Vector MachinesBayes Point MachinesClustering AlgorithmsSummary
Part III: Practical Applications
Chapter 7: Building Customer Propensity Models
The Business ProblemData Acquisition and PreparationData AnalysisTraining the ModelModel Testing and ValidationModel PerformancePrioritizing Evaluation MetricsSummary
Chapter 8: Visualizing Your Models with Power BI
OverviewIntroducing Power BIThree Approaches for Visualizing with Power BIScoring Your Data in Azure Machine Learning and Visualizing in ExcelScoring and Visualizing Your Data in ExcelScoring Your Data in Azure Machine Learning and Visualizing in powerbi.comLoading DataBuilding Your DashboardSummary
Chapter 9: 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 10: 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 11: Building Predictive Maintenance Models
OverviewPredictive Maintenance ScenariosThe Business ProblemData Acquisition and PreparationThe DatasetData LoadingData AnalysisTraining the ModelModel Testing and ValidationModel PerformanceTechniques for Improving the ModelUpsampling and DownsamplingModel DeploymentCreating a Predictive ExperimentPublishing Your Experiment as a Web ServiceSummary
Chapter 12: Recommendation Systems
OverviewRecommendation Systems Approaches and ScenariosThe Business ProblemData Acquisition and PreparationThe DatasetTraining the ModelModel Testing and ValidationSummary
Chapter 13: Consuming and Publishing Models on Azure Marketplace
What Are Machine Learning APIs?How to Use an API from Azure MarketplacePublishing Your Own Models in Azure MarketplaceCreating and Publishing a Web Service for Your Machine Learning ModelCreating Scoring ExperimentPublishing Your Experiment as a Web ServiceObtaining the API Key and the Details of the OData EndpointPublishing Your Model as an API in Azure MarketplaceSummary
Chapter 14: Cortana Analytics
What Is the Cortana Analytics Suite?Capabilities of Cortana Analytics SuiteExample ScenarioSummary
Index

Content preview from Predictive Analytics with Microsoft Azure Machine Learning, Second Edition

CHAPTER 11

Building Predictive Maintenance Models

Leading manufacturers are now investing in predictive maintenance, which holds the potential to reduce cost, increase margin and customer satisfaction. Though traditional techniques from statistics and manufacturing have helped, the industry is still plagued by serious quality issues and the high cost of business disruption when components fail. Advances in machine learning offer a unique opportunity to reduce cost and improve customer satisfaction. This chapter will show how to build models for predictive maintenance using Microsoft Azure Machine Learning. Through examples we will demonstrate ...

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Predictive Analytics with Microsoft Azure Machine Learning, Second Edition

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.