Bootcamp — Weekly schedule

Week-by-week schedule of the AIRINA Labs Machine Learning & AI Bootcamp.

The operational schedule for one full-time cohort (ten weeks). Part-time cohorts run the same content over twenty weeks at half the weekly load. Per-cohort dates fill in at intake; the structure below is stable across cohorts.

For program rationale, prerequisites, and the ten-module curriculum overview, see the syllabus. For the final project, see the capstone brief.

</tr> </tbody> </table> </div> ## How the week pages work Every week page is generated from a single source — [`_data/bootcamp.yml`](https://github.com/gabayae/gabayae.github.io/blob/main/_data/bootcamp.yml) — so the data lives in one place. Each week page renders: 1. **What you ship this week** — the deliverable, due date, submission channel, rubric. 2. **Live sessions and labs** — the default weekly cadence (Mon–Thu live + lab blocks, Friday speaker / lab review / retrospective), with per-cohort dates and Zoom links filled in inline. 3. **Learning outcomes and topics covered.** 4. **Labs** — three to four hands-on projects per week, with the dataset link and the task spec. 5. **Readings** — split into mandatory (with the "before X" day specified) and optional deepening. 6. **Catalogue cross-references** — back to the existing [course notes](/courses/) when there is a corresponding course. ## Operational notes - Sessions run on **Africa/Lagos time (UTC+1)** by default. Part-time cohorts negotiate times at intake. - Lab notebooks live in a [public GitHub repo](https://github.com/AI-Technipreneurs) that mirrors this schedule. Each week's lab is a tagged release. - Capstone milestones are interleaved with the regular modules: proposal in week 4, midterm review in week 7, code freeze week 9, final presentations week 10. - Past-cohort recordings are linked on each week page once the session is complete and retained for at least twelve months.
Week Module Title Deliverable Detail
01 M1 Python for data work
Take the Python you already half-know and make it precise enough to ship. 		Public GitHub repo with the cleaned-up Lab-1 refactor: README.md, <code class="... week 01 →
02 M2 Introduction to machine learning
What ML actually is, what it isn't, and the workflow that runs underneath every project. 		Notebook + 600-word writeup: a clean train/validation/test pipeline on a real hospital dataset, with calibration analysis and an explicit... week 02 →
03 M3 Classical ML --- regression, classification, clustering
The pre-deep-learning toolkit. Still the right answer for most tabular problems. 		Credit-scoring pipeline on an African bank dataset, with EDA notebook, model comparison (logistic + XGBoost), calibration, fairness audit... week 03 →
04 M4 Recommender systems
How Netflix-style systems actually work, and the honest evaluation problem they create. 		Hybrid recommender on a real e-commerce dataset, evaluated against three offline metrics with an explicit discussion of why they disagree. week 04 →
05 M5 Natural language processing
From the linguistics-aware classical methods to the transformer-era pipeline. 		Three-deliverable pack: a sentiment classifier (logistic baseline + DistilBERT), an NER tagger on a multilingual dataset including an Afr... week 05 →
06 M6 Modern ML --- ANN, CNN, RNN
Deep learning end-to-end, with enough theory to know when *not* to use it. 		Three small deliverables: a 2-layer MLP from scratch in NumPy then ported to PyTorch, a CNN trained on a medical-imaging dataset, and an ... week 06 →
07 M7 LLMs and generative AI
What's under the hood of GPT/Claude/LLaMA, what you can actually do with them, and where they fail. 		Pick one of three tracks: a RAG system over a domain corpus, a LoRA fine-tune of a 1-7B-parameter model on a domain task, or a multi-step... week 07 →
08 M8 MLOps and deployment
What it takes for the model to keep working after the notebook is closed. 		A deployed model: trained model wrapped in a FastAPI service, containerized, deployed to a free-tier cloud, with MLflow tracking and a ba... week 08 →
09 M9 + M10 Capstone work-week + portfolio sessions
Concentrated build week on the capstone, with daily check-ins and three portfolio-craft sessions. 		Capstone code freeze by Friday. Working deployed demo, complete README, half-drafted technical writeup. Plus a personal-website / portfol... week 09 →
10 M9 + M10 Capstone wrap-up, final presentations, portfolio launch
Show what you built, publish what you built, and walk out with a portfolio that says *I can actually do this*. 		Final capstone presentation (10 min talk + 5 min Q&A), public repository at v1.0</...</td> week 10 →