The AI Landscape
A clean map of the AI world. What's real, what's hype, and where you fit.
Next action
Start Lesson 01 — 15–20 min
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What this is
About this micro-course
A 15–20 minute interactive lesson. It sorts out the words you keep hearing. AI is the broad field. ML (machine learning) is the part that learns from examples. Deep learning is the part of ML that uses layered networks. You also get the three phases of AI, in order. Built for people who need language they can defend in a meeting by Friday.
- 5 chapters
- Interactive · self-paced
- Verify First checklist
- No prior AI experience required
What you'll learn
Cut through the noise. By the end you will be able to explain in your own words how AI, machine learning, and deep learning fit together. You will be able to place a tool you already use in one of the three phases: Traditional AI (rules and sorting), Generative AI (writes and makes things), Agentic AI (takes steps on your behalf). And you will be able to run the Verify First checklist on a real output before you act on it.
Curriculum
5 chapters · 15–20 min
AI, ML, and Deep Learning
The nesting doll model that makes the relationships click.
Traditional AI vs. Generative AI vs. Agentic AI
The three phases and where you are now.
From doing the work to designing it
What changes in your role when AI handles the doing, and why it's an upgrade — not a demotion.
The Verify First rule
A checklist for every AI output before it leaves your hands.
Self-assessment
Which phase are you actually using, and what to do next.
Practice assignment
Audit your week
Audit your week for one repetitive task that follows the same pattern each time. Use a Phase 2 tool — that is a generative tool, one that writes or makes something, like a chatbot — to handle the first draft, then evaluate: did it save time? What still required your expertise? Document the result in a one-paragraph debrief.
- Identify one task you do at least weekly that follows the same pattern each time.
- Use any generative AI tool to handle the first draft or summary.
- Apply the Verify First checklist from Chapter 4 before you use the output.
- Write a one-paragraph debrief: time saved, what still needed your judgment, and whether you'd repeat it.
How the practice assignments work
- 1. Finish the lessonEach lesson ends with a short prompt that turns the concept into a one-page deliverable you'd actually use at work.
- 2. Build your artifactUse the copy-paste template inside the course. Most assignments take 15–30 minutes and produce a doc, prompt, or checklist you keep.
- 3. Self-review with the rubricEvery assignment ships with a 4-point rubric (clarity · specificity · safety · usefulness). Score yourself in two minutes.
- 4. Optional: share what you builtBring your artifact to a live session — Office Hours or the weekly build-along — for real-time feedback from our team. Prefer to share publicly? Post it and tag us on LinkedIn (Fear of Becoming Obsolete.).
Assignments are ungraded and self-paced. There's no deadline, no quiz, no certificate gate — the deliverable is the proof you did the work.
Preview · try before you launch
Try this before you launch
“Describe the difference between Traditional AI, Generative AI, and Agentic AI in three sentences — one for each. Use an example from everyday work life for each one.”
What you walk away with
- Explain the difference between AI, machine learning, and deep learning without notes
- Place any tool you use into one of the three phases, and say why
- The Verify First checklist, applied to a real output from your own work
Ready when you are
Start Lesson 01 · 15–20 min