Mastering .NET Machine Learning by Jamie Dixon
English | Mar. 29, 2016 | ISBN: 1785888404 | 358 Pages | AZW3/MOBI/EPUB/PDF (conv) | 42.39 MB

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

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

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.