AI For Everyone

Chapters

1Orientation and Course Overview

2AI Fundamentals for Everyone

3Machine Learning Essentials

4Understanding Data

5AI Terminology and Mental Models

AI, ML, and DL definitions Models and algorithms Parameters and hyperparameters Loss functions and metrics Training, validation, and testing Precision, recall, and F1 ROC curves and AUC Confusion matrix basics Embeddings and representations Transfer learning overview Fine-tuning concepts Generative vs discriminative Prompting fundamentals Retrieval-augmented generation Interpretability at a glance

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

15AI and Society, Careers, and Next Steps

Courses/AI For Everyone/AI Terminology and Mental Models

AI Terminology and Mental Models

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Build a shared vocabulary and simple mental models for AI discussions.

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Training, validation, and testing

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