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.