AI For Everyone

Chapters

1Orientation and Course Overview

2AI Fundamentals for Everyone

3Machine Learning Essentials

4Understanding Data

5AI Terminology and Mental Models

6What Makes an AI-Driven Organization

7Capabilities and Limits of Machine Learning

8Non-Technical Deep Learning

9Workflows for ML and Data Science

10Choosing and Scoping AI Projects

11Working with AI Teams and Tools

12Case Studies: Smart Speaker and Self-Driving Car

13AI Transformation Playbook

14Pitfalls, Risks, and Responsible AI

Sources of bias Fairness concepts Bias mitigation approaches Adversarial attack basics Robustness testing methods Privacy risks and harms Misuse and adverse applications Responsible AI frameworks Transparency and explainability Human oversight practices Red teaming and stress tests Incident response planning Model and data documentation Legal and regulatory context Ethics review checklists

15AI and Society, Careers, and Next Steps

Courses/AI For Everyone/Pitfalls, Risks, and Responsible AI

Pitfalls, Risks, and Responsible AI

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Identify and mitigate ethical, technical, and operational risks.

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Adversarial attack basics

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