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LEADER 00000cam a2200937Ii 4500
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049 INap
082 04 005.74
082 04 005.74|223
099 eBook O'Reilly for Public Libraries
100 1 Barga, Roger S.,|eauthor.
245 10 Predictive analytics with Microsoft Azure machine learning
:|bbuild and deploy actionable solutions in minutes /
|cRoger Barga, Valentine Fontama and Wee Hyong Tok.
|h[O'Reilly electronic resource]
250 Second edition.
264 1 [Berkley, CA] :|bApress,|c2015.
264 4 |c©2015
300 1 online resource
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Includes index.
505 00 |gMachine generated contents note:|gpt. I|tIntroducing
Data Science and Microsoft Azure Machine Learning --|gch.
1|tIntroduction to Data Science --|tWhat is Data Science?
--|tAnalytics Spectrum --|tDescriptive Analysis --
|tDiagnostic Analysis --|tPredictive Analysis --
|tPrescriptive Analysis --|tWhy Does It Matter and Why
Now? --|tData as a Competitive Asset --|tIncreased
Customer Demand --|tIncreased Awareness of Data Mining
Technologies --|tAccess to More Data --|tFaster and
Cheaper Processing Power --|tData Science Process --
|tCommon Data Science Techniques --|tClassification
Algorithms --|tClustering Algorithms --|tRegression
Algorithms --|tSimulation --|tContent Analysis --
|tRecommendation Engines --|tCutting Edge of Data Science
--|tRise of Ensemble Models --|tSummary --|tBibliography -
-|gch. 2|tIntroducing Microsoft Azure Machine Learning --
|tHello, Machine Learning Studio! --|tComponents of an
Experiment --|tIntroducing the Gallery --|tFive Easy Steps
to Creating a Training Experiment --|gStep 1|tGetting the
Data --|gStep 2|tPreprocessing the Data --|gStep 3
|tDefining the Features --|gStep 4|tChoosing and Applying
Machine Learning Algorithms --|gStep 5|tPredicting Over
New Data --|tDeploying Your Model in Production --
|tCreating a Predictive Experiment --|tPublishing Your
Experiment as a Web Service --|tAccessing the Azure
Machine Learning Web Service --|tSummary --|gch. 3|tData
Preparation --|tData Cleaning and Processing --|tGetting
to Know Your Data --|tMissing and Null Values --|tHandling
Duplicate Records --|tIdentifying and Removing Outliers --
|tFeature Normalization --|tDealing with Class Imbalance -
-|tFeature Selection --|tFeature Engineering --|tBinning
Data --|tCurse of Dimensionality --|tSummary --|gch. 4
|tIntegration with R --|tR in a Nutshell --|tBuilding and
Deploying Your First R Script --|tUsing R for Data
Preprocessing --|tUsing a Script Bundle (ZIP) --|tBuilding
and Deploying a Decision Tree Using R --|tSummary --|gch.
5|tIntegration with Python --|tOverview --|tPython
Jumpstart --|tUsing Python in Azure ML Experiments --
|tUsing Python for Data Preprocessing --|tCombining Data
using Python --|tHandling Missing Data Using Python --
|tFeature Selection Using Python --|tRunning Python Code
in an Azure ML Experiment --|tSummary --|gpt. II
|tStatistical and Machine Learning Algorithms --|gch. 6
|tIntroduction to Statistical and Machine Learning
Algorithms --|tRegression Algorithms --|tLinear Regression
--|tNeural Networks --|tDecision Trees --|tBoosted
Decision Trees --|tClassification Algorithms --|tSupport
Vector Machines --|tBayes Point Machines --|tClustering
Algorithms --|tSummary --|gpt. III|tPractical Applications
--|gch. 7|tBuilding Customer Propensity Models --
|tBusiness Problem --|tData Acquisition and Preparation --
|tData Analysis --|tTraining the Model --|tModel Testing
and Validation --|tModel Performance --|tPrioritizing
Evaluation Metrics --|tSummary --|gch. 8|tVisualizing Your
Models with Power BI --|tOverview --|tIntroducing Power BI
--|tThree Approaches for Visualizing with Power BI --
|tScoring Your Data in Azure Machine Learning and
Visualizing in Excel --|tScoring and Visualizing Your Data
in Excel --|tScoring Your Data in Azure Machine Learning
and Visualizing in powerbi.com --|tLoading Data --
|tBuilding Your Dashboard --|tSummary --|gch. 9|tBuilding
Churn Models --|tChurn Models in a Nutshell --|tBuilding
and Deploying a Customer Churn Model --|tPreparing and
Understanding Data --|tData Preprocessing and Feature
Selection --|tClassification Model for Predicting Churn --
|tEvaluating the Performance of the Customer Churn Models
--|tSummary --|gch. 10|tCustomer Segmentation Models --
|tCustomer Segmentation Models in a Nutshell --|tBuilding
and Deploying Your First K-Means Clustering Model --
|tFeature Hashing --|tIdentifying the Right Features --
|tProperties of K-Means Clustering --|tCustomer
Segmentation of Wholesale Customers --|tLoading the Data
from the UCI Machine Learning Repository --|tUsing K-Means
Clustering for Wholesale Customer Segmentation --|tCluster
Assignment for New Data --|tSummary --|gch. 11|tBuilding
Predictive Maintenance Models --|tOverview --|tPredictive
Maintenance Scenarios --|tBusiness Problem --|tData
Acquisition and Preparation --|tDataset --|tData Loading -
-|tData Analysis --|tTraining the Model --|tModel Testing
and Validation --|tModel Performance --|tTechniques for
Improving the Model --|tUpsampling and Downsampling --
|tModel Deployment --|tCreating a Predictive Experiment --
|tPublishing Your Experiment as a Web Service --|tSummary
--|gch. 12|tRecommendation Systems --|tOverview --
|tRecommendation Systems Approaches and Scenarios --
|tBusiness Problem --|tData Acquisition and Preparation --
|tDataset --|tTraining the Model --|tModel Testing and
Validation --|tSummary --|gch. 13|tConsuming and
Publishing Models on Azure Marketplace --|tWhat Are
Machine Learning APIs? --|tHow to Use an API from Azure
Marketplace --|tPublishing Your Own Models in Azure
Marketplace --|tCreating and Publishing a Web Service for
Your Machine Learning Model --|tCreating Scoring
Experiment --|tPublishing Your Experiment as a Web Service
--|tObtaining the API Key and the Details of the OData
Endpoint --|tPublishing Your Model as an API in Azure
Marketplace --|tSummary --|gch. 14|tCortana Analytics --
|tWhat Is the Cortana Analytics Suite? --|tCapabilities of
Cortana Analytics Suite --|tExample Scenario --|tSummary.
520 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.
588 0 Online resource; title from PDF title page (EBSCO, viewed
August 31, 2015).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
630 00 Windows Azure.
630 07 Windows Azure|2fast
650 0 Information technology|xManagement.
650 6 Technologie de l'information|xGestion.
650 7 Software Engineering.|2bicssc
650 7 Data mining.|2bicssc
650 7 Program concepts|xlearning to program.|2bicssc
650 7 Information technology|xManagement|2fast
653 00 computerwetenschappen
653 00 computer sciences
653 00 datamining
653 00 data mining
653 10 Information and Communication Technology (General)
653 10 Informatie- en communicatietechnologie (algemeen)
700 1 Fontama, Valentine,|eauthor.
700 1 Tok, Wee-Hyong,|eauthor.
776 08 |iPrinted edition:|z9781484212011
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781484212004/?ar
|zAvailable on O'Reilly for Public Libraries
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938 Coutts Information Services|bCOUT|n32459834
938 EBL - Ebook Library|bEBLB|nEBL4178070
938 EBSCOhost|bEBSC|n1057093
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