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Data Science in the Cloud with Microsoft Azure Machine Learning and R By Stephen Elston

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 							Azure ML, Microsofts Azure Machine Learning cloud platform, provides an easy-to-use and powerful set of
cloud-based data transformation and machine-learning tools. With this OReilly report, youll learn the basics of
manipulating data and constructing and evaluating models in Azure ML, illustrated by a complete data science example.
  
  Author Stephen Elston from Quantia Analytics performs the required data manipulation throughout the report,
and then constructs and evaluates a regression model for a bicycle-sharing-demand dataset. You can follow along by
downloading the code and data provided. Youll then learn how to publish your trained models as web services in the Azure
cloud.
  
  This report will show you how:
  
  Azure ML provides a powerful environment for the
creation and cloud deployment of predictive analytic solutions
  R code is readily integrated into the Azure ML
workflow
  Careful development, selection, and filtering of features is the key to most data science problems

 Recognizing business goals and requirements is essential to creating a successful data science solution
  A
complete understanding of residuals is essential to the evaluation of predictive model performance
  Stephen F.
Elston, Managing Director of Quantia Analytics, LLC, is a big-data geek and data scientist, with over two decades of
experience using R and S/SPLUS for predictive analytics and machine learning. Hes been developing, selling, and
supporting analytics software for over two decades.
   						

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