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Microsoft Azure Machine Learning

By : Sumit Mund

4 (5)

Microsoft Azure Machine Learning

4 (5)

By: Sumit Mund

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Introduction

1. Introduction

Introduction to predictive analytics

Machine learning

Introduction to Azure Machine Learning

Summary

2. ML Studio Inside Out

2. ML Studio Inside Out

Introduction to ML Studio

Getting started with Microsoft Azure

Inside ML Studio

Workspace as a collaborative environment

Summary

3. Data Exploration and Visualization

3. Data Exploration and Visualization

The basic concepts

Data exploration in ML Studio

Do it yourself

Summary

4. Getting Data in and out of ML Studio

4. Getting Data in and out of ML Studio

Getting data in ML Studio

Data format conversion

Getting data from ML Studio

Summary

5. Data Preparation

5. Data Preparation

Data manipulation

Splitting data

Do it yourself

The Apply SQL Transformation module

Advanced data preprocessing

Summary

6. Regression Models

6. Regression Models

Understanding regression algorithms

Train, score, and evaluate

Linear regression

Optimizing parameters for a learner – the sweep parameters module

The train neural network regression – do it yourself

Comparing models with the evaluate model

Other regression algorithms

No free lunch

Summary

7. Classification Models

7. Classification Models

Understanding classification

Training, scoring, and evaluating modules

Classifying diabetes or not

Predicting adult income with decision-tree-based models

Do it yourself – comparing models to choose the best

Multiclass classification

Multiclass classification with the Iris dataset

Multiclass classification with the Wine dataset

Summary

8. Clustering

8. Clustering

Understanding the K-means clustering algorithm

Creating a K-means clustering model using ML Studio

Do it yourself

Clustering versus classification

Summary

9. A Recommender System

9. A Recommender System

The Matchbox recommender

Building a recommendation system

Summary

10. Extensibility with R and Python

10. Extensibility with R and Python

Introduction to R

Introduction to Python

Why should you extend through R/Python code?

Extending experiments using the Python language

Extending experiments using the R language

Summary

11. Publishing a Model as a Web Service

11. Publishing a Model as a Web Service

Preparing an experiment to be published

Creating a scoring experiment

Specifying the input and output of the web service

Publishing a model as a web service

Consuming a published web service

Web service configuration

Updating the web service

Summary

12. Case Study Exercise I

12. Case Study Exercise I

Problem definition and scope

The dataset

Data exploration and preparation

Model development

Model deployment

Summary

13. Case Study Exercise II

13. Case Study Exercise II

Problem definition and scope

The dataset

Data exploration and preparation

Model development

Model deployment

Summary

Index

Index

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "This means that the azureml_main entry point function can import these modules directly."

A block of code is set as follows:

def azureml_main(dataframe1 = None, dataframe2 = None):
    #Get all the columns
    cols = dataframe1.columns.tolist()
    #Select columns with name starting with letter 'm'
    dataframe1=dataframe1[[col for col in cols if col.startswith('m')]]
    #Return the modified dataset
    return dataframe1

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The Filter Based Feature Selection module can identify the most important features in a dataset."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Programming languages

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