The discovering ML.NET series continues. With the release of v0.3.0, it is time to look at performing K-means clustering using F# and Microsoft’s new ML.NET framework. The use case will be to use examination attributes to classify mammogram results.
F# and ML.Net Classification
Read Time: 12 minutesExpanding on my previous post, F# and ML.NET Regression, the current post will take a look at performing classification using Microsoft’s new ML.NET framework. The task at hand will be to use biomechanical attributes to classify patient vertebra conditions into normal (NO), disk hernia (DH), or spondilolysthesis (SL) categories.
F# and ML.Net Regression
Read Time: 13 minutesRecently Microsoft announced ML.NET, a machine learning framework for .NET. This is exciting news. So my mind immediately goes to: how does this look with F#? The current post will take a look at using ML.NET’s regression module to predict concrete compressive strength based on its composite ingredients.
F# and Word Stems
Read Time: 6 minutesToday’s post discusses performing word stemming with F#. This will be an expansion on a previous post, Comparing Quora question intent. As a result, it will also address some feature engineering.
F# and DotEnv
Read Time: 3 minutesOften the question arises, what is the best way to manage environment variables for a project. Many languages have libaries precisely for this issue. A common approach is the use of a .env file, .NET and F# are no different. In today’s post I’ll take a brief look at leveraging DotEnv in an F# application.