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01 - The 12-month arc, honest version

What this session is

The real shape of 12 months. Not "learn AI in 30 days." Not "transformer specialist in 6 weeks." The actual phases, the actual time each takes, and which months are demoralizing.

The four phases

Months 1-2: Foundation

What you do: Python you can write from memory. Linux comfortable. Git for real. NumPy and pandas. Read about ML at a conceptual level. No models trained yet.

What it feels like: Slow. You're not "doing AI." You'll wonder if you picked the wrong path. You haven't. The people who skip this phase plateau at month 4.

Signal you're ready for phase 2: You can write a Python script that loads a CSV, does some pandas operations, and saves the result. Without Googling syntax.

Months 3-5: Hands-on ML and DL

What you do: Train a logistic regression, a small neural net, an MLP, a CNN on MNIST/CIFAR. Read the PyTorch docs front-to-back. Build the things in AI Systems From Scratch. Fine-tune a small open model. Run RAG.

What it feels like: Fun. Confusing. You'll keep hitting "but why" walls. You'll Google a lot. You'll feel like you're memorizing magic incantations.

Signal you're ready for phase 3: You can explain, out loud, what backprop roughly does and why a learning rate matters.

Months 6-9: Specialization + portfolio

What you do: Pick a specialization (see page 7). Build two real projects (page 10). Read 5 papers in your area. Start contributing to one OSS project. Build in public.

What it feels like: Lonely. Slow visible progress. Mostly hard work with no audience yet.

Signal you're ready for phase 4: You have one project that's live, written up, and you can demo end-to-end in 5 minutes.

What you do: Apply, interview, fail, learn, repeat. Continue contributing to OSS. Continue building. Network.

What it feels like: Bruising. Most applications get no reply. The interviews you do get teach you what's missing. You go back to building, then re-apply.

Signal you're ready to ship: Offer in hand. Or, more realistically: a couple of final-round interviews, one of which converts.

What's not on this list

  • "Read every paper on arXiv." You can't. Don't try.
  • "Master CUDA." Optional. Mostly not needed.
  • "Get a PhD." Different job.
  • "Learn every framework." Pick one stack, get deep.

The demoralizing months

Statistically, the people who quit do so around:

  • Month 2 - "this is too much math/code, maybe I'm not smart enough." (You are. Push through.)
  • Month 6 - "I've been doing this for half a year and I don't feel hireable." (You aren't yet. That's fine. Two more phases to go.)
  • Month 10 - "I've applied to 80 jobs and nobody's writing back." (Yes. That's the job search. Don't stop.)

If you know these slumps are coming, they hurt less.

What you can accelerate, and what you can't

Accelerate: Programming. Linux. Tooling. Code-reading speed. Project shipping pace.

Cannot accelerate: Building real intuition for what models do and don't do. That's reps. Lots of small experiments, watching loss curves, breaking things. There's no shortcut.

If you have less than 12 months

Compress, but don't skip. Phase 1 is the only one you can shorten meaningfully (by leveraging existing programming skill). The rest scale with calendar weeks, not hours per week - your brain needs sleep between concepts.

People who go faster than 6 months usually had a programming + math background already.

What you might wonder

"Can I do this part-time?" Yes. Most people do. Add 50-100% to every timeline. 15-20 hours/week sustained beats 40 hours/week for 3 weeks and burn out.

"Should I quit my job?" Usually no. Pay for the runway with your current job. The first AI engineering offer often pays less than your current senior backend role does. Plan for that.

"What about bootcamps?" Some are good. Most are expensive and teach exactly what's on YouTube. If you're disciplined, you don't need one. If you're not, a bootcamp's structure may be worth the money.

Done

  • Know the four phases.
  • Know the slumps to expect.
  • Have a realistic calendar.

Next: Math you actually need →

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