Book Image

Mastering .NET Machine Learning

By : Jamie Dixon, Damian R Mingle
Book Image

Mastering .NET Machine Learning

By: Jamie Dixon, Damian R Mingle

Overview of this book

.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.
Table of Contents (18 chapters)
Mastering .NET Machine Learning
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Combining data


Sometimes the data that you obtain from a source system is incomplete. Consider this dataset of crash locations that was obtained from the State Department of Transportation office:

Notice that latitude and longitude are missing and that location does not use a normal address/city/state pattern. Rather, it is OnRoad, Miles, FromRoad, and TowardRoad. Unfortunately, this is fairly common when getting data from public entities—systems may have been built before lat/lon became mainstream and the system's addressing might be designed to only work inside the system. This means we need a way to figure out the latitude and longitude from this atypical addressing.

If you pull the source code down from the site, you will see a couple of script files. The first is called BingGeocode. This is a script that goes out to the Bing maps API and returns a geolocation for a given address. The key thing is that, although Bing does not recognize OnRoad/FromRoad/TowardRoad, it does recognize cross...