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Data science in the cloud with Microsoft Azure machine learning and Python

Author: Stephen F Elston
Publisher: Sebastopol, CA : O'Reilly Media, [2016] ©2016
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:
Take time to explore Microsoft's Azure machine learning platform, Azure ML—a production environment that simplifies the development and deployment of machine learning models. In this O'Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML.  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Stephen F Elston
OCLC Number: 1040037770
Description: 1 online resource (1 volume) : illustrations
Responsibility: Stephen F. Elston.

Abstract:

Take time to explore Microsoft's Azure machine learning platform, Azure ML—a production environment that simplifies the development and deployment of machine learning models. In this O'Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML. The report walks you through key steps in the data science process from problem definition, data understanding, and feature engineering, through construction of a regression model and presentation of results. You'll also learn how to extend Azure ML with Python. Elston uses downloadable Python code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance. At the end, you'll learn how to publish your trained models as web services in the Azure cloud. With this report, you'll learn how to: Navigate Azure ML Studio Use the Python Script module Load Python modules from a zip file Use the Sweep Parameters module Apply a SQL transformation Use the Cross Validate Model module Publish a scoring model as a web service to Excel Use Jupyter Notebooks with Azure ML.

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