{"product_id":"9781430267669","title":"Machine Learning Projects for .NET Developers","description":"\u003cp\u003e\u003cem\u003eMachine Learning Projects for .NET Developers\u003c\/em\u003e 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.\u003c\/p\u003e\u003cp\u003eIn a series of fascinating projects, you’ll learn how to:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBuild an optical character recognition (OCR) system from scratch\u003c\/li\u003e\n\u003cli\u003eCode a spam filter that learns by example\u003c\/li\u003e\n\u003cli\u003eUse F#’s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language)\u003c\/li\u003e\n\u003cli\u003eTransform your data into informative features, and use them to make accurate predictions\u003c\/li\u003e\n\u003cli\u003eFind patterns in data when you don’t know what you’re looking for\u003c\/li\u003e\n\u003cli\u003ePredict numerical values using regression models\u003c\/li\u003e\n\u003cli\u003eImplement an intelligent game that learns how to play from experience\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eAlong 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.\u003c\/p\u003eWhat you’ll learn\u003cul\u003e\n\u003cli\u003eLearn vocabulary and landscape of machine learning\u003c\/li\u003e\n\u003cli\u003eRecognize patterns in problems and how to solve them\u003c\/li\u003e\n\u003cli\u003eLearn simple prediction algorithms and how to apply them\u003c\/li\u003e\n\u003cli\u003eDevelop, diagnose and tune your models\u003c\/li\u003e\n\u003cli\u003eWrite elegant, efficient and bug-free functional code with F#\u003cp\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003e\u003cem\u003eMachine Learning Projects for .NET Developers\u003c\/em\u003e 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.\u003c\/p\u003e\u003cp\u003eWhile 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. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eTable of Contents\u003cp\u003eChapter 1: \u003cem\u003e256 Shades of Gray\u003c\/em\u003e\u003cem\u003e:\u003c\/em\u003e Building A Program to Automatically Recognize Images of Numbers\u003c\/p\u003e\u003cp\u003eChapter 2: \u003cem\u003eSpam or Ham?\u003c\/em\u003e Detecting Spam in Text Using Bayes' Theorem\u003c\/p\u003e\u003cp\u003eChapter 3: \u003cem\u003eThe Joy of Type Providers:\u003c\/em\u003e Finding and Preparing Data, From Anywhere\u003c\/p\u003e\u003cp\u003eChapter 4: \u003cem\u003eOf Bikes and Men:\u003c\/em\u003e Fitting a Regression Model to Data with Gradient Descent\u003c\/p\u003e\u003cp\u003eChapter 5: \u003cem\u003eYou Are Not An Unique Snowflake:\u003c\/em\u003e Detecting Patterns with Clustering and Principle Component Analysis\u003c\/p\u003e\u003cp\u003eChapter 6: \u003cem\u003eTrees and Forests:\u003c\/em\u003e Making Predictions from Incomplete Data \u003c\/p\u003e\u003cp\u003eChapter 7: \u003cem\u003eA Strange Game:\u003c\/em\u003e Learning From Experience with Reinforcement Learning\u003c\/p\u003e\u003cp\u003eChapter 8: \u003cem\u003eDigits, Revisited:\u003c\/em\u003e Optimizing and Scaling Your Algorithm Code\u003c\/p\u003e\u003cp\u003eChapter 9: Conclusion\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Apress","offers":[{"title":"Default Title","offer_id":47124701905136,"sku":"9781430267669","price":39.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781430267669_p0.jpg?v=1763750446","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781430267669","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}