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Microsoft Azure Machine Learning

You're reading from Microsoft Azure Machine Learning

Product type Book

Published in Jun 2015

Publisher

ISBN-13 9781784390792

Pages 212 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Sumit Mund

Christina Storm

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Table of Contents (21) Chapters

Microsoft Azure Machine Learning

Credits

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

Preface

Introduction

ML Studio Inside Out

Data Exploration and Visualization

Getting Data in and out of ML Studio

Data Preparation

Regression Models

Classification Models

Clustering

A Recommender System

Extensibility with R and Python

Publishing a Model as a Web Service

Case Study Exercise I

Case Study Exercise II

Index

Consuming a published web service

Now that the web service has been published and is available as an API, you can write a program in the language of your choice and consume the API to get a prediction or the result of the API. The API needs an API key, which you can find on the published web service dashboard page (refer to 4). Without the API key, you won't be able to connect to the web service. On the API help page, you will find the detailed documentation on the published API, including the sample code in C#.Net, Python, and R, which include the necessary inline comments. You can find the links to the API help page from the same web service dashboard page (refer to 5 and 6).

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Authors (2)

Sumit Mund

Sumit Mund is a BI/analytics consultant with about a decade of industry experience. He works in his own company, Mund Consulting Ltd., where he is a director and lead consultant. He is an expert in machine learning, predictive analytics, C#, R, and Python programming; he also has an active interest in Artificial Intelligence. He has extensive experience working with most of Microsoft Data Analytics tools and also on Big Data platforms, such as Hadoop and Spark. He is a Microsoft Certified Solution Expert (MCSE in Business Intelligence). Sumit regularly engages on social media platforms through his tweets, blogs, and LinkedIn profile, and often gives talks at industry conferences and local user group meetings.

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Christina Storm

contacted on 5 aug 16

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