Mastering .NET Machine Learning

	Title:	Mastering .NET Machine Learning
	Authors:	Jamie Dixon
	Edition:	1st – 2016
	Publisher:	Packt Publishing
	Pages:	358
	Language:	English
	ISBN-10	1785888404
	ISBN-13	978-1785888403
	Format:	PDF
	Size (MB):	10
//rcm-na.amazon-adsystem.com/e/cm?lt1=_blank&bc1=000000&IS2=1&bg1=FFFFFF&fc1=000000&lc1=0000FF&t=freeengineebo-20&o=15&p=8&l=as4&m=amazon&f=ifr&ref=as_ss_li_til&asins=1785888404&linkId=2575e0710ddb515551317ec268627c1c	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 Naïve 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 Copyright Disclaimer: This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we’ll remove relevant links or contents immediately.