Section outline
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Introduction
This section introduces what machine learning is and how it fits into day-to-day work in data science. You’ll learn how ML differs from traditional analytics and the kinds of problems ML is designed to solve. By understanding common task types and the typical workflow and roles, you’ll be better prepared to collaborate on ML projects and identify where ML adds business value.
Learning Objectives
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Distinguish machine learning from traditional analytics in a data science context.
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Identify common ML task types (prediction, classification, clustering) and when to use each.
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Describe a typical ML workflow, key team roles, and where ML creates value (automation, personalization, risk).
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