Section outline
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Introduction
This section builds the essential vocabulary you’ll use to understand and communicate about machine learning work in a data science setting. You’ll learn how datasets are represented for modeling, what it means to “train” a model, and why performance can change outside the lab. These concepts form the foundation for reading ML documentation, collaborating with teams, and validating results responsibly.
Learning Objectives
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Identify features, targets/labels, and training examples in a supervised ML dataset.
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Distinguish models, parameters, and hyperparameters, and describe what happens during training.
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Explain generalization, basic evaluation metrics, and why validation matters before production use.
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