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Azure Machine Learning Studio for The Non-Data Scientist: Learn

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Estado
En muy buen estado: Libro que se ha leído y que no tiene un aspecto nuevo, pero que está en un ...
Title
Azure Machine Learning Studio for The Non-Data Scientist: Learn
ISBN
9781548871123
Categoría

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Product Identifiers

Publisher
CreateSpace
ISBN-10
1548871125
ISBN-13
9781548871123
eBay Product ID (ePID)
6038712619

Product Key Features

Number of Pages
158 Pages
Publication Name
Azure Machine Learning Studio for the Non-Data Scientist : Learn How to Create Experiments, Operationalize Them Using Excel and Angular . Net Core Applications, and Create Retraining Programs to Improve Predictive Results
Language
English
Subject
Computer Science
Publication Year
2017
Type
Textbook
Author
Michael Washington
Subject Area
Computers
Format
Trade Paperback

Dimensions

Item Height
0.4 in
Item Weight
13 Oz
Item Length
9.2 in
Item Width
7.5 in

Additional Product Features

Intended Audience
Trade
Synopsis
Creating predictive models is no longer relegated to data scientists when you use tools such as the Microsoft Azure Machine Learning Studio. Azure Machine Learning Studio is a web browser-based application that allows you to create and deploy predictive models as web services that can be consumed by custom applications and other tools such as Microsoft Excel. With this book, you will learn how to create predictive experiments, operationalize them using Excel and Angular .Net Core applications, and create retraining programs to improve predictive results. Table of Contents Chapter 1: The Author is Not a Data Scientist * Why Do We Need Predictive Modeling? * An Introduction to Get You Started Chapter 2: An End-To-End Azure Machine Learning Studio Application * Create an Azure Machine Learning Workspace * Create An Experiment * Select Columns * Split Data * Train The Model * Score The Model * Evaluate The Model * Create A Predictive Web Service * Consume The Model Using Excel Chapter 3: An Angular 2 .Net Core Application Consuming an Azure Machine Learning Model * The Application * Creating The Application * Create The .Net Core Application * Add PrimeNG * Add The Database * Create Code To Call Azure Machine Learning Web Service * Create The Angular Application * Saving Data * Viewing Data Chapter 4: Retraining an Azure Machine Learning Application * The Retraining Process * Prepare The Training Data * Set-up An Azure Storage Account * Create The Batch Retraining Program * Get Required Values * Add A New Endpoint And Patch It * Consume The New Endpoint, Creating predictive models is no longer relegated to data scientists when you use tools such as the Microsoft Azure Machine Learning Studio.Azure Machine Learning Studio is a web browser-based application that allows you to create and deploy predictive models as web services that can be consumed by custom applications and other tools such as Microsoft Excel.With this book, you will learn how to create predictive experiments, operationalize them using Excel and Angular .Net Core applications, and create retraining programs to improve predictive results.Table of ContentsChapter 1: The Author is Not a Data Scientist* Why Do We Need Predictive Modeling?* An Introduction to Get You StartedChapter 2: An End-To-End Azure Machine Learning Studio Application* Create an Azure Machine Learning Workspace* Create An Experiment* Select Columns* Split Data* Train The Model* Score The Model* Evaluate The Model* Create A Predictive Web Service* Consume The Model Using ExcelChapter 3: An Angular 2 .Net Core Application Consuming an Azure Machine Learning Model* The Application* Creating The Application* Create The .Net Core Application* Add PrimeNG* Add The Database* Create Code To Call Azure Machine Learning Web Service* Create The Angular Application* Saving Data* Viewing DataChapter 4: Retraining an Azure Machine Learning Application* The Retraining Process* Prepare The Training Data* Set-up An Azure Storage Account* Create The Batch Retraining Program* Get Required Values* Add A New Endpoint And Patch It* Consume The New Endpoint

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