Machine Learning Made Easy – An Introduction to ML.NET
summary
Overview of ML.NET 1.0 with live coding: shows building a Titanic survival classifier using MLContext and pipelines, with data loading, missing-value imputation, one‑hot encoding, feature concatenation and logistic regression, all wired into a saved model and PredictionEngine. Manual pipeline reaches about 80% accuracy; ML.NET AutoML finds a better model (~82% after 40 seconds). Notes that ML.NET runs cross‑platform, consumes TensorFlow models (no native deep‑training yet), and that a working pipeline fits in roughly forty lines of code.
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