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Using ML.NET to Build Machine Learning Models

Using Built-In Unsupervised Learning Tasks

Explore how to apply ML.NET's built-in unsupervised learning tasks including anomaly detection and clustering. Understand the process of loading data, detecting seasonality, identifying anomalies, and grouping data entries without labeled outcomes using ML.NET tools and code.

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ML.NET comes with the following built-in tasks for unsupervised learning:

  • Anomaly detection: The goal is to look at any arbitrary set of data and identify any records that don’t seem to match the overall pattern.

  • Clustering: The goal of this task is to look at arbitrary data, find similarities between different entries, and arrange the entries into groups based on similarities. ...