Today we’re announcing ML.NET 1.1 which includes updates for ML.NET (v1.0 was released on May 2019) and Model Builder for Visual Studio. Following are the key highlights...
In this article we will use ML.NET to build and compare four Machine Learning Binary Classification pipelines. Each model uses another algorithm to predict the quality of wine from 11 physicochemical features. The characteristics of the prediction models are visualized using OxyPlot. All the code is in C# (“Look mom, no Python!”) and hosted in a UWP app together with some other ML.NET use cases.
Today, coinciding with //BUILD 2019/ conference, we’re thrilled by launching ML.NET 1.0 release! You can read the official ML.NET 1.0 release announcement Blog Post here and get started at the ML.NET site here. In this blog post I’m providing quite a few additional technical details along with my personal vision that you might find interesting, though.
Clustering is a well known type of unsupervised machine learning algorithm. It is unsupervised since there isn't usually a known label in the data to help the algorithm know how to train on a known value. Instead of training on the data point to see a pattern in how it got a label value, an unsupervised algorithm will find patterns among each of the data points themselves. In this post, I'll go over how to use the clustering trainer in ML.NET.