Free, hands-on AI courses that run anywhere, from the team building Synapsa, an AI-native learning platform.

Looking for the tutorials this repository carried from 2017 to 2025? They are in archive/, unchanged.

Every lesson here is a notebook you do, not one you read. You fill in the exercises, and each one checks itself the moment you run it. Hints stay hidden until you ask for them. Every lesson also has an autograded rubric with partial credit and a worked solution, both in its folder in this repository. Some lessons are C or C++ exercises, built and graded the same way. Open any of them in Google Colab, Kaggle, Binder, GitHub Codespaces or your own Jupyter: the first cell installs what the lesson needs at the versions it was measured with, and does nothing where it is already there.

The courses are aimed at the work people are actually paid for: evidence an inspector can re-run, a model a risk committee can sign off, an alarm a plant manager will trust. Every number a lesson prints is computed by code you run, and every claim it makes about the world cites a primary source.

Where to start

The model-risk, predictive-maintenance and document-intelligence courses build on lessons/T00-L01-the-8gb-track, which makes the profiler and the compute tiers every lesson is held to; start there if you have not done it. Each programme's MODULES.md is its map and shows which lessons exist and which are still only specified; the humanoid lab's is its README.md, and lessons/ has its own.

What exists today

46 lessons, every one passing all 14 gates in QUALITY.md and independently reviewed. 7 are compiled C or C++ exercises. Every notebook opens in Colab, Kaggle, Binder, Codespaces or local Jupyter. Every MODULES.md marks a module built only when its lesson exists and has passed; everything else says specified, and means it.

How a lesson works

Press Run all before you have written a line. Every cell still runs: each unfinished exercise says it is not implemented yet, and the notebook ends with a progress board, one line per exercise, marked ✅ passed, ❌ failed or ⏳ not started. Fill in an exercise and re-run its cell, and its check tells you whether you are right, and if not, what the likely mistake is. Each exercise has two hints: the first says what to think about, the second the approach in words. Neither gives you the answer. To score your work against the full rubric, clone the repository and run python tools/grade.py <lesson folder>.

Anything a lesson measures on your machine, like a time or a memory peak, prints with its unit and will differ from ours. Every other number a finished lesson prints is identical on Python 3.11 and 3.12, and the workflow above re-checks that on Linux, which is what Colab, Kaggle, Binder and Codespaces run.

On Kaggle, a lesson that installs a package or fetches a file needs Internet switched on in the notebook's settings, and Kaggle allows that only for phone-verified accounts. Colab, Binder and Codespaces need nothing.

To work locally instead, with Python 3.11 or 3.12:

python -m venv .venv && .venv/bin/pip install -r requirements.txt
.venv/bin/jupyter lab

On Windows the two commands are .venv\Scripts\pip and .venv\Scripts\jupyter.

The bar every lesson clears

QUALITY.md sets out 14 gates. The short version:

• measurable objectives, and prerequisites that are real lessons;
• scaffolded exercises with instant feedback, an autograded rubric, a worked solution and a self-check;
• runs top to bottom in a fresh interpreter, inside a declared compute tier and time budget;
• data that is free and needs no registration, named with its licence;
• every claim about the world cites a primary source, and no measured or computed number in the prose is typed by hand;
• the notebook opens and runs alone on Python 3.11 and 3.12, and Run all is a good experience.

A lesson is marked built only after an independent reviewer has tried to break it, including by grading plausible wrong answers against its rubric.

For maintainers

• lessons/, flagships/, programmes/: track lessons, flagship subtrees, industry programmes.
• tools/:
  • execute.py runs a lesson's solution within its declared budget and writes back what it measured;
  • grade.py runs the autograder (--solution grades the reference);
  • notebooks.py generates each lesson.ipynb and its launcher cell (--check for drift);
  • verify_portable.py runs each notebook alone in a minimal kernel on Python 3.11 and 3.12;
  • build_student_bundle.py builds what a student receives and fails on any solution leak;
  • status.py generates the status tables above and in each MODULES.md;
  • verify_all.py machine-checks every lesson against what a machine can judge of gates 1-12.
  • brand_lockups.py redraws the lockups in brand/ from the Synapsa wordmark's geometry.
• .github/workflows/lessons.yml runs all of it on Linux on every change outside archive/.
• brand/ holds the Synapsa Commons lockups used here and at the top of every notebook.

The 2017-2025 tutorials

The tutorials that first made this repository popular are in archive/, unchanged and with their history. GitHub does not redirect moved files, so an old link of the form .../blob/master/<path> no longer resolves; replace master with legacy-tutorials, the tag that keeps the old layout, and it will.

Licence

Synapsa Commons is © 2026 RealAI and licensed under the Apache License 2.0; see LICENSE and NOTICE. The tutorials in archive/ keep the grant they were published under, in archive/LICENSE-2017. Assets authored for a lesson state their own terms in that lesson's assets/SOURCE.md, and third-party material keeps its own licence. The Synapsa name and logo belong to RealAI and are not covered by the licence.