Section outline
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Introduction
This section walks through the end-to-end machine learning pipeline as it’s typically practiced in data science teams. You’ll learn how to frame ML problems, assess whether your data supports the goal, and structure experiments that produce trustworthy results. These skills help you translate business questions into models that can be evaluated and deployed responsibly in real products and workflows.
Learning Objectives
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Frame a data science problem as prediction, classification, or grouping with a clear target and success criteria.
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Identify common data readiness risks, including leakage and sampling bias, and describe how they affect model outcomes.
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Set up train/validation/test splits and select evaluation metrics that align with business impact.
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