Introduction to Artificial Intelligence

What's covered

7 modules · 39 lessons

1. 1. Module 0

   • Course Introduction

2. 2. Module 1: What AI Actually Is (and Isn't)

   • Module 1 Overview
   • Lesson 1.1: The Word "AI" Has Three Meanings
   • Lesson 1.2: A Short History That Actually Matters
   • Lesson 1.3: What Large Language Models Are Doing
   • Lesson 1.4: Capabilities, Limits, and the Hype Cycle
   • Simulation: Spot the Hype
   • Module 1 Knowledge Check

3. 3. Module 2: How Modern AI Systems Work

   • Module 2 Overview
   • Lesson 2.1: Training, Inference, and Why the Distinction Matters
   • Lesson 2.2: Tokens, Context Windows, and Why Models Forget
   • Lesson 2.3: Temperature, Sampling, and Determinism
   • Lesson 2.4: Multimodal Models
   • Hands-On: Read a Model Card
   • Module 2 Knowledge Check

4. 4. Module 3: Prompting as a Skill

   • Module 3 Overview
   • Lesson 3.1: Anatomy of a Good Prompt
   • Lesson 3.2: From One-Shot to Few-Shot
   • Lesson 3.3: Structured Prompting Techniques
   • Lesson 3.4: Prompt Failure Modes
   • Simulation: Fix the Failing Prompt
   • Module 3 Knowledge Check

5. 5. Module 4: From Chatbots to AI Workflows

   • Module 4 Overview
   • Lesson 4.1: Why Models Need Help (and What Kind)
   • Lesson 4.2: Retrieval-Augmented Generation (RAG)
   • Lesson 4.3: Tool Use and Function Calling
   • Lesson 4.4: Agents and Multi-Step Workflows
   • Lesson 4.5: Choosing Between Prompting, RAG, and Fine-Tuning
   • Module 4 Knowledge Check

6. 6. Module 5: Using AI Responsibly in Real Work

   • Module 5 Overview
   • Lesson 5.1: Hallucination, Bias, and Reliability
   • Lesson 5.2: Data, Privacy, and IP
   • Lesson 5.3: Building an Evaluation Habit
   • Lesson 5.4: Adoption Without the Pilot Trap
   • Lesson 5.5: Your First 90 Days
   • Simulation: Pick the Right AI Approach
   • Module 5 Knowledge Check

7. 7. Final Assessment & Capstone

   • Final Knowledge Check
   • Capstone: AI Use-Case Proposal