Bite-Sized Chunks

Suggested Paths

For beginners, the biggest risk isn't picking the "wrong" topic — it's overwhelm and quitting. Pick a goal, follow the order. Each track starts from a shared Python + CS + Math foundation.

Everyone starts here

Foundation · ~3 months part-time

Everyone starts here. Python builds confidence fast because you see results immediately; CS gives you the vocabulary every other course assumes; Math is a slow background drip — not a blocker.

  1. 1

    Python

    Programming Languages · Beginner · 1,289 lessons

    First language. Quick wins build the habit before anything harder.

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  2. 2

    Computer Science

    Foundations · Intermediate · 2,872 lessons

    Light pass — variables, functions, data structures, complexity. The vocabulary downstream courses assume.

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  3. 3

    Mathematics

    Foundations · Beginner · 3,121 lessons

    Background drip: discrete logic, basic stats, a touch of linear algebra. Don't wait to finish before moving on.

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Then pick a track

Web DeveloperData / MLBackend / InfrastructureAI Engineer (product)Security Specialist

Data / ML

~9–12 months part-time after foundation

If you want to reason about data — not just run libraries. Order here is non-negotiable: stats before ML, every time. Lean harder on the Math foundation (especially statistics and linear algebra) before starting this track.

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  1. 1

    Data Science

    AI & Data · Intermediate · 2,145 lessons

    pandas, statistics in practice, visualization, storytelling. Career-grade on its own if you stop here.

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  2. 2

    Machine Learning and Deep Learning

    AI & Data · Advanced · 3,538 lessons

    Graduate-flavored. Don't rush — the math from the foundation is what makes this make sense.

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  3. 3

    AI Engineering

    AI & Data · Advanced · 1,886 lessons

    Apply what you've learned: LLMs, RAG, agents, evals.

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Beginner-protection rules

Apply these regardless of which track you pick.

  • Don't wait to finish Math before moving on — keep it as a background track.
  • Ship one real app before circling back to Security and System Design.
  • Don't rush ML/DL — without stats, you'll run libraries you can't reason about.
  • Pick one language at a time. Switching too early kills momentum.
  • Pick a track and commit for at least a month before second-guessing it. Overwhelm and quitting are the real risks — not picking the 'wrong' path.

Want to skip around instead? Browse all courses.