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

ML project lifecycle Problem framing steps Data collection planning Labeling and QA process Baseline model first Error analysis loops Iteration and ablation Experiment tracking habits Model selection criteria Evaluation plan design Deployment plan outline Monitoring and alerts Feedback and retraining Data science workflow map Collaboration checkpoints

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

15AI and Society, Careers, and Next Steps

Courses/AI For Everyone/Workflows for ML and Data Science

Workflows for ML and Data Science

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Learn practical, repeatable workflows that drive successful AI projects.

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Evaluation plan design

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