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  • Book Overview & Buying Microsoft Azure Machine Learning
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Microsoft Azure Machine Learning

Microsoft Azure Machine Learning

By : Sumit Mund
4 (5)
Buy this Book
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Microsoft Azure Machine Learning

Microsoft Azure Machine Learning

4 (5)
By: Sumit Mund
Buy this Book

Overview of this book

The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction
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1. Introduction
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Introduction to predictive analytics
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Machine learning
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Introduction to Azure Machine Learning
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Summary
2
2. ML Studio Inside Out
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2. ML Studio Inside Out
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Introduction to ML Studio
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Getting started with Microsoft Azure
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Inside ML Studio
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Workspace as a collaborative environment
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Summary
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3. Data Exploration and Visualization
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3. Data Exploration and Visualization
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The basic concepts
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Data exploration in ML Studio
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Do it yourself
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Summary
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4. Getting Data in and out of ML Studio
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4. Getting Data in and out of ML Studio
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Getting data in ML Studio
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Data format conversion
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Getting data from ML Studio
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Summary
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5. Data Preparation
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5. Data Preparation
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Data manipulation
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Splitting data
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Do it yourself
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The Apply SQL Transformation module
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Advanced data preprocessing
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Summary
6
6. Regression Models
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6. Regression Models
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Understanding regression algorithms
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Train, score, and evaluate
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Linear regression
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Optimizing parameters for a learner – the sweep parameters module
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The train neural network regression – do it yourself
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Comparing models with the evaluate model
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Other regression algorithms
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No free lunch
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Summary
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7. Classification Models
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7. Classification Models
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Understanding classification
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Training, scoring, and evaluating modules
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Classifying diabetes or not
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Predicting adult income with decision-tree-based models
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Do it yourself – comparing models to choose the best
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Multiclass classification
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Multiclass classification with the Iris dataset
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Multiclass classification with the Wine dataset
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Summary
8
8. Clustering
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8. Clustering
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Understanding the K-means clustering algorithm
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Creating a K-means clustering model using ML Studio
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Do it yourself
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Clustering versus classification
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Summary
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9. A Recommender System
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9. A Recommender System
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The Matchbox recommender
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Building a recommendation system
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Summary
10
10. Extensibility with R and Python
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10. Extensibility with R and Python
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Introduction to R
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Introduction to Python
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Why should you extend through R/Python code?
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Extending experiments using the Python language
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Extending experiments using the R language
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Summary
11
11. Publishing a Model as a Web Service
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11. Publishing a Model as a Web Service
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Preparing an experiment to be published
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Creating a scoring experiment
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Specifying the input and output of the web service
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Publishing a model as a web service
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Consuming a published web service
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Web service configuration
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Updating the web service
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Summary
12
12. Case Study Exercise I
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12. Case Study Exercise I
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Problem definition and scope
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The dataset
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Data exploration and preparation
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Model development
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Model deployment
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Summary
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13. Case Study Exercise II
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13. Case Study Exercise II
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Problem definition and scope
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The dataset
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Data exploration and preparation
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Model development
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Model deployment
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Summary
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Index
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Index

Model deployment

Before publishing your model as a web service, create a scoring experiment and make sure that the Web service input and the Web service output modules are properly connected. Also, process the model if you want your output in a proper format, for example, outputting only the scored label and input to the score model may not contain the label column; in this case, you can also filter the ArrDelay15 column along with the others in the Project Columns module connected to the Score Model module. Let's take a look at the following screenshot:

Model deployment

Then, publish your scoring experiment, so that it would be available as a web service in the staging environment. Test your web service visually and also with Excel. If you are good at programming, you're encouraged to test your web service by writing a small client program as well.

CONTINUE READING
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Tech Concepts
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Programming languages
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Microsoft Azure Machine Learning
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