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

Overview

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.

Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.

What’s New in the Second Edition?

Five new chapters have been added with practical detailed coverage of:

Python Integration – a new feature announced February 2015

Data preparation and feature selection

Data visualization with Power BI

Recommendation engines

Selling your models on Azure Marketplace

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