All resources Course · Built in public

The AI Track

This is how I'm actually learning to build AI that moves revenue. The sales and marketing systems, and the engineering that runs them. I'm doing it in public and keeping only what holds up in real work. Every module ends in something I shipped, not a course I finished.

13 modules 2 tracks 0 shipped so far

Your progress

0/13

Don't want the whole course? If you just want the raw material, here's my reading list, in order.

Everyone calls themselves an AI expert now. I’d rather prove it.

So here’s something a little exposed. I’m publishing the whole curriculum I’m using to go from “twenty years in demand gen who builds AI systems” to “the person you’d actually trust to architect, build, and ship AI for revenue.” And I’m logging the real path through it, one module and one artifact at a time, including the parts I get wrong.

This is the map. Every module below turns into its own post, with something built at the end and a running note on how it actually went.

What I’m after, and what I’m not

I’m building practitioner-grade fluency. Enough to architect a system, write the code, make the cost and quality calls, and hold my own with a VP of Sales and a senior engineer in the same meeting.

I’m not chasing research-grade depth. No deriving backprop, no proving anything converges, no PhD math. My job isn’t to publish papers. It’s to ship AI systems that answer to a number: pipeline, revenue, meetings booked.

Every module has a depth target so I don’t over-invest where it doesn’t pay off. Conversational means I can explain it to a client and know when it applies. Working means I can build it and debug it with the docs open. Deep means I can design it from scratch and teach it, and that’s where my edge is.

And every module ends with something I build and ship. Watching teaches almost nothing. Building teaches everything. The things I build double as case studies and tools I can hand a client.

The two tracks

Track 1 is the machine learning floor. A few weeks, enough to be credible and not make rookie mistakes. It isn’t the job, so I move fast. Track 2 is the real work, building AI for revenue, and it runs for months and then never really stops, because the field moves every month.

The full module list, with depth targets, time estimates, and your own progress, is laid out below.

The cadence

  • Weeks 1 to 6: Track 1. Fast and conceptual. Don’t polish it to death.
  • Months 2 to 4: Track 2, modules 6 through 10, in order. The core of it.
  • Months 4 to 6: modules 11 through 13, and going deeper on 12 with real client work.
  • After that: a few hours a week on primary sources, and one thing shipped every month.

The one rule I’m holding myself to: no tutorial hell. A module isn’t done when I’ve watched the videos. It’s done when the thing I built is live.

Follow along. I’ll be honest about what’s hard.

Track 1

Baseline ML

  1. 1
    How ML actually works

    🟢 Conversational · 3–4 days

    Soon
  2. 2
    Classic models, and when to use them

    🟢 Conversational · 4–5 days

    Soon
  3. 3
    Data: the real job

    🟡 Working · 5–6 days

    Soon
  4. 4
    Evaluation and metrics: where most 'AI experts' are frauds

    🔴 Deep · 1 week

    Soon
  5. 5
    Neural networks, by intuition

    🟢→🟡 Bridge · 4–5 days

    Soon
Track 2

Applied AI

  1. 6
    How LLMs actually work

    🟡 Working · 1 week

    Soon
  2. 7
    Prompt and context engineering: the daily craft

    🔴 Deep · 1–2 weeks

    Soon
  3. 8
    RAG: grounded, cited answers

    🔴 Deep · 2 weeks

    Soon
  4. 9
    Tools and agents: from chatbot to system

    🔴 Deep · 2 weeks

    Soon
  5. 10
    Evals and reliability: the pro's edge

    🔴 Deep · 1–2 weeks

    Soon
  6. 11
    Fine-tune vs. RAG vs. prompt: knowing what not to build

    🟡 Working · 4–5 days

    Soon
  7. 12
    Applied AI for go-to-market: my home turf

    🔴 Deep · Ongoing

    Soon
  8. 13
    AI engineering and deployment: ship it like an engineer

    🟡 Working · 1–2 weeks

    Soon

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