Mastering .NET Machine Learning

Jamie Dixon

• Publisher: Packt Publishing
• Year: 2016
• eBook ISBN: 9781785888403
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Master the art of machine learning with.NET and gain insight into real-world applications

About This Book

• Based on.NET framework 4.6.1, includes examples on ASP.NET Core 1.0
• Set up your business application to start using machine learning techniques
• Familiarize the user with some of the more common.NET libraries for machine learning
• Implement several common machine learning techniques
• Evaluate, optimize and adjust machine learning models

Who This Book Is For

This book is targeted at.Net developers who want to build complex machine learning systems. Some basic understanding of data science is required.

What You Will Learn

• Write your own machine learning applications and experiments using the latest.NET framework, including.NET Core 1.0
• Set up your business application to start using machine learning.
• Accurately predict the future using regressions.
• Discover hidden patterns using decision trees.
• Acquire, prepare, and combine datasets to drive insights.
• Optimize business throughput using Bayes Classifier.
• Discover (more) hidden patterns using KNN and Naive Bayes.
• Discover (even more) hidden patterns using K-Means and PCA.
• Use Neural Networks to improve business decision making while using the latest ASP.NET technologies.
• Explore "Big Data", distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adapting
• Along the way, learn about Open Data, Bing maps, and MBrace

In Detail

.Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their.Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines.

This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using.NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in.NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions.

You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results.

Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly

Style and approach

This book will guide you in learning everything about how to tackle the flood of data being encountered these days in your.NET applications with the help of popular machine learning libraries offered by the.NET framework.

Mastering .NET Machine Learning

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Table of Contents

Mastering .NET Machine Learning

Credits

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Welcome to Machine Learning Using the .NET Framework

What is machine learning?

Why .NET?

What version of the .NET Framework are we using?

Why write your own?

Why open data?

Why F#?

Getting ready for machine learning

Setting up Visual Studio

Learning F#

Third-party libraries

Math.NET

Accord.NET

Numl

Summary

2. AdventureWorks Regression

Simple linear regression

Setting up the environment

Preparing the test data

Standard deviation

Pearson's correlation

Linear regression

Math.NET

Regression try 1

Regression try 2

Accord.NET

Regression

Regression evaluation using RMSE

Regression and the real world

Regression against actual data

AdventureWorks app

Setting up the environment

Updating the existing web project

Implementing the regression

Summary

3. More AdventureWorks Regression

Introduction to multiple linear regression

Intro example

Keep adding x variables?

AdventureWorks data

Adding multiple regression to our production application

Considerations when using multiple x variables

Adding a third x variable to our model

Logistic regression

Intro to logistic regression

Adding another x variable

Applying a logistic regression to AdventureWorks data

Categorical data

Attachment point

Analyzing results of the logistic regression

Adding logistic regression to the application

Summary

4. Traffic Stops – Barking Up the Wrong Tree?

The scientific process

Open data

Hack-4-Good

FsLab and type providers

Data exploration

Visualization

Decision trees

Accord

numl

Summary

5. Time Out – Obtaining Data

Overview

SQL Server providers

Non-type provider

SqlProvider

Deedle

MicrosoftSqlProvider

SQL Server type provider wrap up

Non SQL type providers

Combining data

Parallelism

JSON type provider – authentication

Summary

6. AdventureWorks Redux – k-NN and Naïve Bayes Classifiers

k-Nearest Neighbors (k-NN)

k-NN example

Naïve Bayes

Naïve Bayes in action

One thing to keep in mind while using Naïve Bayes

AdventureWorks

Getting the data ready

k-NN and AdventureWorks data

Naïve Bayes and AdventureWorks data

Making use of our discoveries

Getting the data ready

Expanding features

Summary

7. Traffic Stops and Crash Locations – When Two Datasets Are Better Than One

Unsupervised learning

k-means

Principle Component Analysis (PCA)

Traffic stop and crash exploration

Preparing the script and the data

Geolocation analysis

PCA

Analysis summary

The Code-4-Good application

Machine learning assembly

The UI

Adding distance calculations

Augmenting with human observations

Summary

8. Feature Selection and Optimization

Cleaning data

Selecting data

Collinearity

Feature selection

Normalization

Scaling

Overfitting and cross validation

Cross validation – train versus test

Cross validation – the random and mean test

Cross validation – the confusion matrix and AUC

Cross validation – unrelated variables

Summary

9. AdventureWorks Production – Neural Networks

Neural networks

Background

Neural network demo

Neural network – try #1

Neural network – try #2

Building the application

Setting up the models

Building the UX

Summary

10. Big Data and IoT

AdventureWorks and the Internet of Bikes

Data considerations

MapReduce

MBrace

Distributed logistic regression

The IoT

PCL linear regression

Service layer

Universal Windows app and Raspberry Pi 2

Next steps

Summary

Index

Mastering .NET Machine Learning

Copyright © 2016 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: March 2016

Production reference: 1210316

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78588-840-3

www.packtpub.com

Credits

Author

Jamie Dixon

Reviewers

Reed Copsey, Jr.

César Roberto de Souza

Commissioning Editor

Vedika Naik

Acquisition Editor

Meeta Rajani

Technical Editor

Pankaj Kadam

Copy Editor

Laxmi Subramanian

Proofreader

Safis Editing

Indexer

Rekha Nair

Graphics

Jason Monteiro

Production Coordinator

Aparna Bhagat

Cover Work

Aparna Bhagat

About the Author

Jamie Dixon has been writing code for as long as he can remember and has been getting paid to do it since 1995. He was using C# and JavaScript almost exclusively until discovering F#, and now combines all three languages for the problem at hand. He has a passion for discovering overlooked gems in datasets and merging software engineering techniques to scientific computing. When he codes for fun, he spends his time using Phidgets, Netduinos, and Raspberry Pis or spending time in Kaggle competitions using F# or R.

Jamie is a bachelor of science in computer science and has been an F# MVP since 2014. He is the former chair of his town's Information Services Advisory Board and is an outspoken advocate of open data. He is also invol...

Table of contents

• Mastering .NET Machine Learning