ADVANCED · TECHNICAL SPECIALIST

Advanced ML &
Generative AI

Master the technical foundations of machine learning and build real-world generative AI systems — from transformer architectures to production deployment.

DURATION16 Weeks

LEVELAdvanced — Technical Professional

AWARDProfessional Certificate in ML & Generative AI

PREREQUISITESPython, basic ML or Data Analytics

DELIVERYOnline live + self-paced labs

NEXT INTAKEQuarterly cohorts

Apply Now → All Programmes

PROGRAMME IMPACT

Graduates placed in ML/AI roles87%

Average salary uplift post-programme2.4×

Industry projects completed340+

African countries represented22

Programme Overview

Generative AI is the most consequential technological shift of our era — and the continent that learns to build with it, not just consume it, will lead the next wave of the digital economy. This intensive 16-week programme takes technically capable professionals from solid ML foundations to hands-on mastery of large language models, diffusion models, multimodal AI, and production GenAI systems.

Every module is grounded in practical African use cases: from building Swahili-language chatbots to training crop disease detection models on satellite imagery. You will leave not just knowing how these systems work, but having built several of them — with a portfolio of deployable projects to prove it.

What You Will Study

MODULE 01

Deep Learning Foundations

Neural network architectures, backpropagation, regularisation, and optimisation. Hands-on implementation in PyTorch. Covers CNNs, RNNs, and attention mechanisms.

MODULE 02

Natural Language Processing & LLMs

Tokenisation, embeddings, transformer architecture in depth. Fine-tuning and prompting open-source LLMs (LLaMA, Mistral). Building multilingual NLP systems for African languages.

MODULE 03

Generative AI: Diffusion & Multimodal Models

How diffusion models work. Stable Diffusion, image generation pipelines, multimodal models (vision-language). Building generative applications from scratch.

MODULE 04

Retrieval-Augmented Generation (RAG)

Vector databases, semantic search, RAG pipeline design. Building domain-specific AI assistants grounded in private knowledge bases.

MODULE 05

MLOps & Production AI Systems

Model versioning, experiment tracking, CI/CD for ML. Deploying models as APIs. Monitoring, drift detection, and responsible production AI.

MODULE 06

Capstone: Build & Ship a GenAI Product

End-to-end project: problem framing, data, model selection, evaluation, deployment. Pitched to a panel of African tech industry leaders.

Learning Outcomes

By the end of this programme, you will be able to:

Design, train, and fine-tune deep learning models for real-world problems

Build and deploy large language model applications using open-source and API-based models

Construct RAG pipelines and domain-specific AI assistants from scratch

Implement diffusion models and multimodal AI systems

Deploy ML models to production using MLOps best practices

Critically evaluate AI systems for bias, fairness, and responsible use

Who Is This For?

Designed for software engineers, data scientists, and technically strong analysts ready to move into specialist ML and AI engineering roles. Requires comfortable programming in Python and familiarity with core ML concepts (covered in the Data Analytics programme or equivalent). Ideal for those targeting roles as ML Engineer, AI Engineer, or GenAI Specialist.

“I had used scikit-learn and built basic models before, but this programme took me into a completely different league. Within three months of graduating I was leading the ML team at a Lagos fintech, building fraud detection and customer churn models that are live in production today.”

— Chukwuemeka O., ML Lead — Lagos, Nigeria