After hitting version 1.0 last month, Microsoft's open source, cross-platform machine learning framework ML.NET has received its first update, which adds functionality and addresses developer concerns about usability and stability.
Dr. James McCaffrey of Microsoft Research uses Python code samples and screenshots to explain naive Bayes classification, a machine learning technique used to predict the class of an item based on two or more categorical predictor variables, such as predicting the gender (0 = male, 1 = female) of a person based on occupation, eye color and nationality.
At its recent Build developer conference, Microsoft highlighted simplified automated machine learning with three approaches: code-first, drag-and-drop and no-code, the latter of which is now accessible via a Web UI in the Azure portal, in preview.
As a prelude to the big Build developer conference next week, Microsoft has announced a host of new development features, many focusing on the Azure cloud and, in particular, artificial intelligence development with machine learning.
Microsoft has updated its open source, cross-platform F# language for functional programming, adding new functionality and positioning it to tackle machine learning projects in the future.
Microsoft just shipped its open source, cross-platform machine learning framework, ML.NET, as a Release Candidate, just one step away from general availability that could come next month.
The Microsoft Azure team today announced several updates to boost artificial intelligence capabilities on the cloud platform, including anomaly detection and object detection in images.