About This BookBased on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0Set up your business application to start using machine learning techniquesFamiliarize the user with some of the more common .NET libraries for machine learningImplement several common machine learning techniquesEvaluate, optimize and adjust machine learning modelsWho This Book Is ForThis book is targeted at .NET developers who want to build complex machine learning systems. Some basic understanding of data science is required.What You Will LearnWrite your own machine learning applications and experiments using the latest .NET Framework, including .NET Core 1.0Set up your business application to start using machine learningAccurately predict the future of your data using simple, multiple, and logistic regressionsDiscover hidden patterns using decision treesAcquire, prepare, and combine datasets to drive insightsOptimize business throughput using Bayes ClassifierDiscover (more) hidden patterns using k-NN and Naive BayesDiscover (even more) hidden patterns using k-means and PCAUse Neural Networks to improve business decision making while using the latest ASP.NET technologiesIn Detail.NET is one of the widely used platforms for developing applications. With the meteoric rise of machine learning, developers are now keen on finding out how to make their .NET applications smarter using machine learning.Mastering .NET Machine Learning is packed with real-world examples to explain how to easily use machine learning techniques in your business applications. You will begin with an introduction to F# and prepare yourselves for machine learning using the .NET Framework. You will then learn how to write a simple linear regression model and, forming a base with the regression model, you will start using machine learning libraries available in .NET Framework such as Math.NET, numl, and Accord.NET with examples. Next, you are going to take a deep dive into obtaining, cleaning, and organizing your data. You will learn the implementation of k-means and PCA using Accord.NET and numl libraries. You will be using Neural Networks, AzureML, and Accord.NET to transform your application into a hybrid scientific application. You will also see how to deal with very large datasets using MBrace and deploy machine learning models to IoT devices so that the machine can learn and adapt on the fly.
Author: Jamie Dixon
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