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Machine learning projects for .NET Developers

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning projects for .NET Developers/ by Mathias Brandewinder.
Author:	Brandewinder, Mathias.
Published:	Berkeley, CA :Apress : : 2015.,
Description:	xix, 300 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-1-4302-6766-9
ISBN:	9781430267669 (electronic bk.)

Machine learning projects for .NET Developers
Brandewinder, Mathias.

Machine learning projects for .NET Developers[electronic resource] /by Mathias Brandewinder. - Berkeley, CA :Apress :2015. - xix, 300 p. :ill., digital ;24 cm.

Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You'll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you're new to F#, this book will give you everything you need to get started. If you're already familiar with F#, this is your chance to put the language into action in an exciting new context. In a series of fascinating projects, you'll learn how to: Build an optical character recognition (OCR) system from scratch Code a spam filter that learns by example Use F#'s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language) Transform your data into informative features, and use them to make accurate predictions Find patterns in data when you don't know what you're looking for Predict numerical values using regression models Implement an intelligent game that learns how to play from experience Along the way, you'll learn fundamental ideas that can be applied in all kinds of real-world contexts and industries, from advertising to finance, medicine, and scientific research. While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you.

ISBN: 9781430267669 (electronic bk.)

Standard No.: 10.1007/978-1-4302-6766-9doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning projects for .NET Developers
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