Section outline
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Introduction
This section consolidates the advanced concepts from the course into a coherent mental model you can apply to Classical ML workflows. You’ll connect modernization tradeoffs, evaluation rigor, and deep learning tooling back to practical decisions in classical settings. The goal is to leave with a clear checklist for choosing, validating, and maintaining models in real deployments.
Learning Objectives
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Synthesize key ideas across modernization, evaluation, and deep learning tools into a unified decision framework.
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Diagnose common failure modes (shift/drift, miscalibration) and select appropriate evaluation signals to monitor them.
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Define a focused next-steps plan for continued learning aligned to Classical ML practice.
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