Section outline
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Introduction
This section introduces what machine learning looks like in practice on real ML teams, including what it can and cannot do. You’ll learn the main problem types and the basic path from raw data to a usable model. This foundation helps you interpret ML work products and collaborate effectively in ML projects.
Learning Objectives
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Distinguish what ML is and is not in a real-world ML team context.
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Identify core ML problem types (supervised, unsupervised, RL) and when each is used.
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Explain how datasets, features, and targets connect to common ML deliverables.
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