Section outline
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Introduction
Machine learning underpins many modern data science workflows by enabling models to learn patterns from data instead of relying on fixed rules. In this section, you’ll build the core vocabulary and mental models needed to understand how ML systems are trained and used in practice. You’ll also connect major learning types to common data science product and analytics use cases.
Learning Objectives
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Distinguish machine learning from rules-based analytics in practical data science contexts.
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Define and correctly use core ML terms: features, labels, training, inference, and models.
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Identify supervised, unsupervised, and semi-supervised learning and match each to common use cases.
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