| Date | Venue | Duration | |
|---|---|---|---|
| 19 - 23 October 2026 | Sandton, Gauteng | 5 Days |
Please note: This is not a final registration, one of our team members will contact you shortly to finalise your registration.
Artificial Intelligence is reshaping how organisations operate and compete. This Fundamentals of Artificial Intelligence (AI): From Theory to Practice practical course builds a clear foundation in AI—starting with core concepts and moving quickly into hands-on work, case studies, and simple prototypes. By the end of the week, you’ll understand how the main AI techniques work, where they add value, and how to start implementing them responsibly in your context.
By the end of this Fundamentals of Artificial Intelligence (AI): From Theory to Practice course, participants will be able to:
Explain the core ideas behind AI, machine learning, deep learning, and NLP in plain language.
Select and apply common ML techniques to classification, regression, and clustering problems.
Build and evaluate simple models using industry-standard tools (e.g., Python, scikit-learn, Keras).
Turn raw data into usable features and interpret model performance correctly.
Use cloud and on-prem options to accelerate AI projects.
Identify ethical, legal (incl. POPIA), and governance considerations and plan for responsible AI use.
Business leaders and managers (HR, Finance, Risk, Operations)
IT professionals
Software developers
Data and BI analysts
Public-sector officials
Students and graduates exploring AI careers
Motivated non-technical professionals seeking a solid, practical introduction to AI
Our diverse instructional approaches ensure effective learning:
– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.
Day 1 — Foundations & impact
What AI is (and isn’t): narrow vs general AI; where we are today.
A brief history: from symbolic AI to modern deep learning.
Where AI delivers value: sector examples (healthcare, finance, retail, public services, supply chain).
Core toolbox tour: ML, neural networks, NLP, computer vision, automation.
Lab: “Hello, AI” — run your first notebook; explore a ready-made model; change inputs and interpret outputs.
Mini-challenge: Spot high-value, low-risk AI opportunities in your organisation.
Day 2 — Machine learning fundamentals
Supervised vs unsupervised vs reinforcement learning.
Key algorithms: linear/logistic regression, decision trees & random forests, k-means clustering, SVMs (conceptual).
Data quality, leakage, train/test splits, cross-validation; metrics (accuracy, precision/recall, F1, ROC-AUC).
Feature engineering: encoding, scaling, text and date features, handling imbalance.
Lab: Build a classification model end-to-end (e.g., churn or credit-risk-style dataset), evaluate properly, avoid common pitfalls.
Mini-challenge: Improve baseline accuracy without overfitting.
Day 3 — Neural networks & deep learning
Neural network basics: layers, activation functions, loss, optimisation, overfitting.
Why deep learning matters and when to (not) use it.
Computer vision with CNNs: image classification workflow.
Lab: Train a simple neural network (Keras); extend to a small CNN for image classification.
Mini-challenge: Boost performance with data augmentation/early stopping.
Day 4 — NLP and the AI toolchain
NLP essentials: tokenisation, embeddings, sequence models, transformers (BERT/GPT concepts).
Use-cases: sentiment analysis, topic tagging, chatbots, document search, speech basics.
Platforms & tooling: TensorFlow, PyTorch, scikit-learn; “AI as a Service” (Azure, Google, AWS) – when cloud makes sense.
Lab options (choose one):
Build a simple sentiment classifier, or
Create a lightweight FAQ chatbot using off-the-shelf embeddings.
Mini-challenge: Compare classical ML vs transformer-based text embeddings on the same problem.
Day 5 — Ethics, governance, deployment & the road ahead
Responsible AI: bias, fairness, transparency, privacy, security; aligning to organisational policy and POPIA.
AI governance in practice: roles, documentation, model monitoring, human-in-the-loop, change management.
From notebook to production: MLOps basics, reproducibility, versioning, model drift.
Future trends: foundation models, multimodal AI, edge AI; skills planning for teams.
Capstone: Team project presentations with feedback and next-steps action plans.
Wrap-up: Personal learning plan, recommended resources, and implementation checklist.
Short daily quizzes, practical labs, and a team capstone.
Prospen Africa Certificate of Completion (CPD-aligned hours available on request).
Discover how our courses enhance professionals’ effectiveness in their workplaces.
The Training was almost excellent and has now contributed in shaping my career and job prospects for the better.
The facilitator was very passionate and course outline was much relevant for my designation at work and for my personal enhancement.
The Facilitator was good and very professional, he outlined the course very well. A very good Job on his part
Very interesting and interactive programme which is needed in the financial industry.
Find answers about course content, certification, registration, and AI skills for executive assistants.
Discover more insights on AI, leadership, digital transformation, and professional development.
Fill in your details and we will send the brochure to your email.
Please check your email for confirmation.
Please check your email for confirmation
