Mastering .NET Machine Learning

Mastering .NET Machine Learning
 Jamie Dixon
1st - 2016
Packt Publishing
Size (MB):

Book Description:
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.

Table of Contents:
1. Welcome to Machine Learning Using the .NET Framework
2. AdventureWorks Regression
3. More AdventureWorks Regression
4. Traffic Stops – Barking Up the Wrong Tree?
5. Time Out – Obtaining Data
6. AdventureWorks Redux – k-NN and Na├»ve Bayes Classifiers
7. Traffic Stops and Crash Locations – When Two Datasets Are 8. Feature Selection and Optimization
9. AdventureWorks Production – Neural Networks
10. Big Data and IoT

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