Applied machine learning
Put a model to work on a real need — and know when not to use one.
- Block code
- DATA-IA
The format
Three months, two hours a day.
- Certificate programme · 128 h
- Two hours a day
- 250,000 F — the same fee for all sixty
- All in the same format, at the same fee.
The qualification
A CQP under MINEFOP accreditation.
- Leads to the CQP — Certificat de Qualification Professionnelle, which the ministry used to call the AQP.
- Awarded under MINEFOP accreditation.
- The full programme for the same profession remains open to anyone aiming for the complete qualification.
Sessions
A new session opens every quarter.
Next intake: Monday 5 October, with enrolment open until 9 October. The following one starts on 4 January 2027.
- First-quarter intake — enrolment closes on 9 October5 Oct.
- Second-quarter intake — 20274 Jan.
Objectives
What you will be able to do.
Frame a machine learning problem and choose the metric that matters to the business
Recognise a problem that should be turned down rather than modelled
Build a reproducible training pipeline
Detect overfitting and apply regularisation
Explain a prediction to a non-specialist
Set up retrieval-augmented search over company documents and evaluate it
Serve a model, monitor its drift and know when to switch it off
Programme
Four modules.
Module 0132 h Module 0232 h Train, evaluate, compare
Module 0332 h Language processing and pre-trained models
Module 0432 h Putting a model into production and monitoring it
Who it is for
Who it is aimed at.
- Learners proficient in Python and data analysis — in practice those who have completed Data engineer — pipelines and data quality or AI-augmented data analyst. A grounding in statistics is required.
Afterwards
Where it leads.
- Junior data scientist, machine learning engineer, designer of augmented document solutions. Overlaps with the ISA programme on retrieval-augmented search.
Method
How it works.
Systematic alternation between short theory sessions and practical work, with most of the time spent on practice
Progression from simple to complex: each module builds on what was learnt in the previous one
Realistic role-plays drawn from Cameroonian business cases
Considered use of generative AI as a working tool, in line with the common core
Regular formative assessments and a final integrative project drawing on all four modules
Assessment
How you are assessed.
Formative assessment at the end of each module (graded role-play and practical exercises)
Practical work assessed against competency sheets
Final integrative project, presented and defended before the trainer
One graded assignment per module in the online course space
Tools
What you work with.
Python, scikit-learn and an interactive notebook
An explainability library (SHAP or equivalent)
A text embedding model and a vector database
A model serving framework and experiment tracking
Business datasets and corporate document collections
Keep exploring
In the same field.
Join the next session
Ready to get started?
Sessions open every quarter. Apply now or request the detailed brochure — our advisers will get back to you.