This guide explains basic concepts and provides tutorials and an API reference for working with ML.NET.
This documentation refers to ML.NET, which is currently in Preview. Material may be subject to change. For more information, see the ML.NET introduction.
To install and start building in ML.NET, follow the Get started tutorial.
To learn about ML.NET, see What is ML.NET?
To understand basics, see Basic concepts for model training in ML.NET.
Analyze sentiment using a binary classification model shows you how to build an app that determines whether sentiment is positive or negative.
Predict taxi fare using a regression model shows you how to build a predictive app that uses many factors from historical data to determine the answer.
Classify iris flowers by features shows you how to use a clustering model to analyze the iris data set.
How to guide
Build a game match-up list app with Infer.NET and probabilistic programming shows you how to build a simplified version of a match-up app like you'd see in an Xbox game.
Machine learning glossary defines key terminology.
Machine learning tasks describes tasks, such as classification and anomaly detection.
Data transforms describes data preparation capabilities in ML.NET.
ML.NET API Reference describes the breadth of APIs available.