Machine Learning Projects for .NET Developers

By Mathias Brandewinder

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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.

Full Description

  • ISBN13: 978-1-4302-6767-6
  • 320 Pages
  • User Level: Intermediate to Advanced
  • Publishing December 16, 2014, but available now as part of the Alpha Program
  • Available eBook Formats: EPUB, MOBI, PDF
  • Print Book Price: $49.99
  • eBook Price: $34.99

Related Titles

Full Description

Software that learns from experience can improve far beyond what a single developer, or even a large team, can write into its code. This is machine learning and it's already influencing a huge range of industries, from advertising to finance, medicine and the most cutting edge scientific research. Machine Learning Projects for .NET Developers is your practical and accessible introduction to this exciting area of software development.

The book emphasizes a functional style of coding that promotes bug-free, reusable code that can be easily parallelized for scalable performance. You’ll code each project in the familiar setting of a C# application, while the machine learning logic uses F#, a language ideally suited to machine learning applications and the logical choice for machine learning 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, discover new techniques, and find out how seamlessly it can integrate with your C# applications.

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 seamlessly with external resources (in this case, useful data analysis tools from the R programming language)
  • Clean up incomplete data and use it to make accurate predictions

  • Build a smart recommendation engine
  • Find patterns in data when you don’t know what you’re looking for
  • Predict numerical values using regression models
  • Accurately spot trends and anomalies

Along the way, you’ll have fun hacking at data, learn fundamental ideas that can be applied in a broad range of real-world contexts, and discover new ways to simplify and approach real-world coding challenges. With Machine Learning Projects for .NET Developers, you'll expand your skill set as a .NET developer, gain a new understanding of data, and have fun working on challenging, mind-expanding problems!

What you’ll learn

  • Learn vocabulary and landscape of machine learning
  • Recognize patterns in problems and how to solve them
  • Learn simple prediction algorithms and how to apply them
  • Develop, diagnose and tune your models
  • Write elegant, efficient and bug-free functional code with F#

Who this book is for

Machine Learning Projects for .NET Developers is for intermediate to advanced .NET developers who are comfortable with C#. No prior experience of machine learning techniques is required. If you’re new to F#, you’ll find everything you need to get started. If you’re already familiar with F#, you’ll find a wealth of new techniques here to interest and inspire you.

While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches and how they can be used in actual code. If you enjoy hacking code and data, this book is for you.

Table of Contents

Table of Contents

Introduction

Chapter 1: Machine Learning Warm-up: Building an Optical Character Recognition System from Scratch

Chapter 2: Spam or Ham? Software that Learns from Text

Chapter 3: The Joy of Type Providers: Using R from F#

Chapter 4: Decision Trees and Random Forests: Making Predictions from Incomplete Data

Chapter 5: Building a Smart Recommendation Engine

Chapter 6: Looking for Patterns in Data with Unsupervised Machine Learning

Chapter 7: Predicting a Number

Chapter 8: Spotting Trends and Anomalies: Analyzing Trending Topics on Twitter

Chapter 9: Where to Go From Here

Appendix

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