Machine Learning Projects for .NET Developers

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.
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

Chapter 1: 256 Shades of Gray: Building A Program to Automatically Recognize Images of Numbers

Chapter 2: Spam or Ham? Detecting Spam in Text Using Bayes' Theorem

Chapter 3: The Joy of Type Providers: Finding and Preparing Data, From Anywhere

Chapter 4: Of Bikes and Men: Fitting a Regression Model to Data with Gradient Descent

Chapter 5: You Are Not An Unique Snowflake: Detecting Patterns with Clustering and Principle Component Analysis

Chapter 6: Trees and Forests: Making Predictions from Incomplete Data

Chapter 7: A Strange Game: Learning From Experience with Reinforcement Learning

Chapter 8: Digits, Revisited: Optimizing and Scaling Your Algorithm Code

Chapter 9: Conclusion