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.
Debug ML Code: Fix, Trace & Evaluate
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了解顶级公司的员工如何掌握热门技能
该课程共有1个模块
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.
涵盖的内容
5个视频3篇阅读材料4个作业
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状态：免费试用Coursera
状态：免费试用Coursera
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