Jamie Dixon

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 datasets 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 is a bachelor of science in computer science and has been an F# MVP since 2014. He is the former chair of his town's Information Services Advisory Board and is an outspoken advocate of open data. He is also involved with his local .NET User Group (TRINUG) with an emphasis on data analytics, machine learning, and the Internet of Things (IoT). Jamie lives in Cary, North Carolina with his wonderful wife Jill and their three awesome children: Sonoma, Sawyer, and Sloan. He blogs weekly at jamessdixon. wordpress.com and can be found on Twitter at @jamie_dixon.

About This Book

Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0

Set up your business application to start using machine learning techniques

Familiarize the user with some of the more common .NET libraries for machine learning

Implement several common machine learning techniques

Evaluate, optimize and adjust machine learning models

Who This Book Is For

This 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 Learn

Write your own machine learning applications and experiments using the latest .NET Framework, including .NET Core 1.0

Set up your business application to start using machine learning

Accurately predict the future of your data using simple, multiple, and logistic regressions

Discover hidden patterns using decision trees

Acquire, prepare, and combine datasets to drive insights

Optimize business throughput using Bayes Classifier

Discover (more) hidden patterns using k-NN and Naive Bayes

Discover (even more) hidden patterns using k-means and PCA

Use Neural Networks to improve business decision making while using the latest ASP.NET technologies