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

Read now

Unlock full access

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 3

Data Preparation

Machine learning can feel magical. You provide Azure ML with training data, select an appropriate leaning algorithm, and it can learn patterns in that data. In many cases, the performance of the model that you build, if done correctly, will outperform a human expert. But, like so many problems in the world, there is a significant “garbage in, garbage out” aspect to machine learning. If the data you give it is rubbish, the learning algorithm is unlikely to be able to overcome it. Machine learning can’t perform “data alchemy” and turn data lead into gold; that’s why we practice good data science, and first clean and enhance ...

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.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

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

Publisher Resources

ISBN: 9781484212004Purchase book

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

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.