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

Summary


If you ask a data scientist what they like least about their day, they will tell you meetings, building slide decks, and munging data in no particular order. Although F# type providers can't help you with meetings and building slide decks, it can decrease the amount of time spent obtaining and cleaning data. Although not completely frictionless, type providers can help you with relational and non-relational data stores and enable you to spend more time with the "fun" parts of data science. Speaking of which, let's jump back into the fun with KNN and Naïve Bayes modeling.