Microsoft Azure Essentials Azure Machine Learning

Cover
Microsoft Press Store
Newsletters
Microsoft Press Guided Tours app
Table of Contents
Foreword
Introduction
	Who should read this book
		Assumptions
	This book might not be for you if…
	Organization of this book
	Conventions and features in this book
	System requirements
	Acknowledgments
	Errata, updates, & support
	Free ebooks from Microsoft Press
	Free training from Microsoft Virtual Academy
	We want to hear from you
	Stay in touch
Chapter 1 Introduction to the science of data
	What is machine learning?
	Today's perfect storm for machine learning
	Predictive analytics
	Endless amounts of machine learning fuel
	Everyday examples of predictive analytics
	Early history of machine learning
	Science fiction becomes reality
	Summary
	Resources
Chapter 2 Getting started with Azure Machine Learning
	Core concepts of Azure Machine Learning
	High-level workflow of Azure Machine Learning
	Azure Machine Learning algorithms
	Supervised learning
	Unsupervised learning
	Deploying a prediction model
	Show me the money
	The what, the how, and the why
	Summary
	Resources
Chapter 3 Using Azure ML Studio
	Azure Machine Learning terminology
	Getting started
	Azure Machine Learning pricing and availability
	Create your first Azure Machine Learning workspace
	Create your first Azure Machine Learning experiment
	Download dataset from a public repository
	Upload data into an Azure Machine Learning experiment
	Create a new Azure Machine Learning experiment
	Visualizing the dataset
	Split up the dataset
	Train the model
	Selecting the column to predict
	Score the model
	Visualize the model results
	Evaluate the model
	Save the experiment
	Preparing the trained model for publishing as a web service
	Create scoring experiment
	Expose the model as a web service
	Azure Machine Learning web service BATCH execution
	Testing the Azure Machine Learning web service
	Publish to Azure Data Marketplace
		Overview of the publishing process
		Guidelines for publishing to Azure Data Marketplace
	Summary
Chapter 4 Creating Azure Machine Learning client and server applications
	Why create Azure Machine Learning client applications?
	Azure Machine Learning web services sample code
	C# console app sample code
	R sample code
	Moving beyond simple clients
	Cross-Origin Resource Sharing and Azure Machine Learning web services
	Create an ASP.NET Azure Machine Learning web client
	Making it easier to test our Azure Machine Learning web service
	Validating the user input
	Create a web service using ASP.NET Web API
	Enabling CORS support
	Processing logic for the Web API web service
	Summary
Chapter 5 Regression analytics
	Linear regression
	Azure Machine Learning linear regression example
	Download sample automobile dataset
	Upload sample automobile dataset
	Create automobile price prediction experiment
	Summary
	Resources
Chapter 6 Cluster analytics
	Unsupervised machine learning
	Cluster analysis
	KNN: K nearest neighbor algorithm
	Clustering modules in Azure ML Studio
	Clustering sample: Grouping wholesale customers
	Operationalizing a K-means clustering experiment
	Summary
	Resources
Chapter 7 The Azure ML Matchbox recommender
	Recommendation engines in use today
	Mechanics of recommendation engines
	Azure Machine Learning Matchbox recommender background
	Azure Machine Learning Matchbox recommender: Restaurant ratings
	Building the restaurant ratings recommender
	Creating a Matchbox recommender web service
	Summary
	Resources
Chapter 8 Retraining Azure ML models
	Workflow for retraining Azure Machine Learning models
	Retraining models in Azure Machine Learning Studio
	Modify original training experiment
	Add an additional web endpoint
	Retrain the model via batch execution service
	Summary
	Resources
About the Author
Free ebooks
Tell us what you think!