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Program

AI & Machine Learning

Eighteen weeks from gradient descent to shipped inference. The hard part of machine learning in practice is not training a model — it is evaluating it honestly and keeping it working once real users touch it. This track spends as much time on evaluation and deployment as on modelling.

  • 18 weeks
  • Advanced
  • On-site in Ibadan
  • Cohort capped at 24
Apply to this trackSee the curriculum

What you will actually do

Eighteen weeks from gradient descent to shipped inference. The hard part of machine learning in practice is not training a model — it is evaluating it honestly and keeping it working once real users touch it. This track spends as much time on evaluation and deployment as on modelling.

Duration
18 weeks
Level
Advanced
Prerequisites
Comfortable with Python and secondary-school mathematics. Our Data Science track is a good route in.
Schedule
Full-time, Monday–Friday, 9am–4pm at Akala Expressway, Ibadan.
Tuition
Upfront, three-part instalments, or an income-share agreement.

Curriculum

Module by module.

Taught in this order because each block depends on the one before it. Nothing is presented as magic you are expected to accept.

  1. Mathematical foundations

    Linear algebra, calculus and probability, taught only as deeply as the models require — but properly, so nothing later is magic.

  2. Classical machine learning

    Supervised and unsupervised methods, feature engineering, and why a gradient-boosted tree still beats a neural network on most tabular problems.

  3. Deep learning with PyTorch

    Networks, training loops, transfer learning, and reading a paper well enough to reimplement it.

  4. LLM applications

    Retrieval-augmented generation, tool use, prompt design and the evaluation harnesses that tell you whether any of it is working.

  5. Evaluation and MLOps

    Metrics that resist gaming, dataset splits that do not leak, plus versioning, monitoring and drift detection in production.

  6. Capstone and career

    Deploy a model behind a real interface with an evaluation report you could defend to a regulator.

Outcomes

What you can do afterwards.

Capabilities, not certificates. Each of these is something you will have done under review before you graduate.

  • Train and fine-tune models in PyTorch
  • Build an LLM application with retrieval and evals
  • Design an evaluation that resists gaming
  • Deploy and monitor a model in production
  • Read and implement a research paper

Roles this feeds into

  • Machine Learning Engineer
  • AI Engineer
  • Research Engineer

92% of graduates are placed within six months, with job support continuing until you sign an offer.

Apply

Apply to AI & Machine Learning

No application fee and no degree required. We reply within five working days.

Include the country code, e.g. +234.

There is no wrong answer — it only tells us where to start you.

A short paragraph is plenty. We read every one.

No application fee. We reply within five working days.