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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
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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
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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
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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
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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
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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
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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

Chapter 1. Introduction

Welcome to the world of predictive analytics and machine learning! Azure Machine Learning enables you to perform predictive analytics with the application of machine learning. Traditionally, it has been an area for experts. Developing and deploying a predictive modeling solution using machine learning has never been simple and easy, even for experts. Microsoft seems to have taken most of the pain out with this new cloud-based offering that allows you to develop and deploy a predictive solution in the simplest and quickest possible way. Even beginners would find it easy and simple to understand.

This chapter, while setting the context for the rest of the book, will present the related topics from a bird's eye view.

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Programming languages
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Microsoft Azure Machine Learning
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