Fundamentals of Artificial Intelligence (AI): From Theory to Practice

Fundamentals of Artificial Intelligence (AI): From Theory to Practice

Building AI capability: principles, techniques, and real-world applications

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Platform:
Online
In-class
Revised and Updated: 23 October 2026
Date Venue Duration
01 - 05 March 2027 Sandton, Gauteng 5 Days
12 - 16 April 2027 Sandton, Gauteng 5 Days
21 - 25 June 2027 Sandton, Gauteng 5 Days

Course Introduction

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.

Course Objectives

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.

Who should attend?

  • 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

Prerequisites and setup

  • Prerequisites: Comfortable with spreadsheets and basic maths. No prior coding required (we include a gentle Python primer).
AI and Digital Management Courses

Training Methodology

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.

Training Outline

Day 1 — Foundations & impact
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
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 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 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
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.

Assessment & certification

  • Short daily quizzes, practical labs, and a team capstone.
  • Prospen Africa Certificate of Completion (CPD-aligned hours available on request).
 

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FAQs – Fundamentals of Artificial Intelligence (AI): From Theory to Practice

Master the fundamentals of artificial intelligence from theory to practice, learning core machine learning concepts, neural network architectures, data preprocessing, model evaluation, and practical deployment strategies to drive intelligent solutions across business operations.

What is covered in the Fundamentals of Artificial Intelligence (AI): From Theory to Practice course?
The course covers foundational concepts of AI, machine learning fundamentals, deep learning basics, natural language processing (NLP), computer vision concepts, prompt engineering, and practical AI implementation strategies across business functions.
Who should attend this Fundamentals of AI Training?
This training is designed for business managers, department heads, IT professionals, operational leaders, analysts, and professionals across sectors seeking to gain a practical, foundational understanding of artificial intelligence capabilities and applications.
How does this course bridge theoretical AI concepts with practical execution?
Delegates transition from core conceptual frameworks to hands-on exercises, analyzing real-world business case studies, evaluating popular AI platforms, and building actionable AI adoption plans tailored to their work environments.
Do delegates need a programming background or prior technical knowledge?
No. The course is specifically structured to be accessible to non-technical professionals, focusing on practical tool utility, business integration logic, and strategic decision-making rather than coding or software engineering.
Does the curriculum address AI governance, ethics, and data protection?
Yes. The course examines ethical AI principles, managing algorithmic bias, ensuring data privacy and compliance under POPIA, and establishing responsible AI governance frameworks within organizations.
Why is gaining a foundational understanding of AI essential for modern professionals?
Understanding fundamental AI principles enables professionals to identify high-value automation opportunities, collaborate effectively with technical teams, make informed technology investments, and drive digital transformation.

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