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Курс от Coursera
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Debug ML Code: Fix, Trace & Evaluate
Курс от CourseraО курсе
Machine learning systems fail in ways that traditional software does not—data changes, schema mismatches, and model assumptions all create unique bugs. This course teaches you how to trace, fix, and validate these issues using a structured debugging workflow. You’ll write targeted unit tests, interpret stack traces and logs, patch defects, and confirm resolutions through regression testing. Each lesson includes concise videos, practical readings, hands-on work, and a realistic ungraded lab. By the end, you’ll know how to diagnose ML failures quickly, prevent regressions, communicate your fixes clearly, and build more reliable ML codebases.
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1 модулей · 15 учебных материалов
01Debug ML Code: Fix, Trace & Evaluate15 материалов
Test to Isolate: Using Unit Tests to Catch ML Defects Early
Welcome: How Testing Helps You Debug ML FasterВидеоYour Experience with ML BugsDIALOGUEWriting Pytest Cases for ML Preprocessing FunctionsВидеоTesting ML Code: Strategies That Reveal Defects EarlyЧтениеУчитесь у экспертов
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