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.
Mathias Brandewinder is a Microsoft MVP for F# based in San Francisco, California. An unashamed math geek, he became interested early on in building models to help others make better decisions using data. He collected graduate degrees in Business, Economics and Operations Research, and fell in love with programming shortly after arriving in the Silicon Valley. He has been developing software professionally since the early days of .NET, developing business applications for a variety of industries, with a focus on predictive models and risk analysis.
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
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