Machine Learning On The .NET Stack

Jamie Dixon

Session: In this session, we will have an overview of machine learning options for the .NET developer. We will consider cloud-based services like Azure Cortana, IBM Watson, and AWS. We will also look at in-process libraries like Accord.NET, numl, and ecog.NET. This session will include portions from my upcoming book from Packt publishing: Machine Learning Using .NET

Bio: Jamie Dixon has been writing code for as long as he can remember and has been getting paid to do it since 1995. He was using C#and javascript almost exclusively until discovering F# and now combines all three languages for the problem at hand. He has a passion for discovering overlooked gems in data sets and merging software engineering techniques to scientific computing. When he codes for fun, he spends his time using Phidgets, Netduinos, and Raspberry Pis or spending time in Kaggle competitions using F# or R. Jamie has a BSCS in Computer Science and a Masters in Public Health. He is the former Chair of his town's Information Services Advisory Board and is an outspoken advocate for Open Data. He also is involved with his local .NET User Group (TRINUG) with an emphasis on data analytics, machine learning, and the internet of things (IoT). He is the author of Mastering .NET Machine Learning and a Microsoft MVP since 2013 Jamie lives in Cary, North Carolina with his wonderful wife Jill and their three awesome children: Sonoma, Sawyer, and Sloan. He blogs at and can be found on Twitter @jamie_dixon

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