Mastering .NET Machine Learning: Use machine learning in your .NET applications - Softcover

Dixon, Jamie

Softcover

ISBN 10: 1785888404 ISBN 13: 9781785888403

Publisher: Packt Publishing, 2016

Synopsis

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 of your data using simple, multiple, and logistic 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 k-NN 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

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 to make their .NET applications smarter using machine learning.

Mastering .NET Machine Learning is packed with real-world examples to explain how to easily use machine learning techniques in your business applications. You will begin with an introduction to F# and prepare yourselves for machine learning using the .NET Framework. You will then learn how to write a simple linear regression model and, forming a base with the regression model, you will start using machine learning libraries available in .NET Framework such as Math.NET, numl, and Accord.NET with examples. Next, you are going to take a deep dive into obtaining, cleaning, and organizing your data. You will learn the implementation of k-means and PCA using Accord.NET and numl libraries. You will be using Neural Networks, AzureML, and Accord.NET to transform your application into a hybrid scientific application. You will also see how to deal with very large datasets using MBrace and deploy machine learning models to IoT devices so that the machine can learn and adapt on the fly.

About this title

About the Author

Jamie Dixon

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 involved with his local .NET User Group (TRINUG) with an emphasis on data analytics, machine learning, and the Internet of Things (IoT). Jamie lives in Cary, North Carolina with his wonderful wife Jill and their three awesome children: Sonoma, Sawyer, and Sloan. He blogs weekly at jamessdixon. wordpress.com and can be found on Twitter at @jamie_dixon.

Edition details

• Publisher: Packt Publishing
• Publication date: 2016
• ISBN 10: 1785888404
• ISBN 13: 9781785888403
• Binding: Paperback
• Language: English
• Number of pages: 358