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Evaluate AI Risks: Adopt Smart Predictions

本课程是 Advanced Project Operations & Automation 专项课程的一部分

位教师：LearningMate

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Evaluate AI risk predictions using performance metrics like precision and recall to make data-driven model adoption decisions.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Advanced Project Operations & Automation 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

Evaluate AI Risks: Adopt Smart Predictions is a beginner-level course designed for project managers, analysts, and business leaders who need to make smart decisions about using AI tools. In a world full of AI hype, how do you prove if a risk prediction model is a valuable asset or a dangerous liability? This course teaches you to look past simple accuracy and use the metrics that matter.

You will learn to apply a professional evaluation framework, using precision and recall to measure an AI model's true performance. Through hands-on activities and guided coaching, you will build a confusion matrix from historical data, calculate these critical metrics, and translate them into clear business terms. The course culminates in creating a concise AI Model Evaluation Report, where you will synthesize your findings to make a definitive 'go/no-go' recommendation. By the end, you won't just be using AI; you'll be able to critically assess its readiness and justify your adoption decisions with hard data.

单元详情

This foundational module introduces the critical concepts needed to evaluate any AI prediction model. You will learn why simple accuracy is often misleading and discover how to frame a model’s performance using a confusion matrix. By the end of this module, you will be able to define precision and recall, the two most important metrics for understanding a model's real-world effectiveness, and classify prediction outcomes correctly.

涵盖的内容

1篇阅读材料2个作业

In this module, you will move from concepts to calculation. You will learn the specific formulas for precision and recall and apply them to a real-world dataset. This hands-on process will transform the raw output of a confusion matrix into two powerful numbers that tell a clear story about the model's performance.

涵盖的内容

1个视频1篇阅读材料2个作业

In this final module, you will learn to synthesize your metrics into a clear, defensible business recommendation. Calculating the numbers is not sufficient; you must interpret what they mean for the project and make the final call: adopt the model, reject it, or send it back for retraining.