Course Contents (Modules)

The course is divided into 12 practical modules plus a capstone project.

Module 1: Introduction to Artificial Intelligence

• What AI is — and what it is not
• History and evolution of AI
• AI vs Machine Learning vs Deep Learning vs Generative AI
• Types of AI: narrow, general and generative
• Real-world AI use cases in UAE industries (banking, real estate, healthcare, government, logistics)
• The AI project lifecycle

Module 2: Python Programming for AI

• Setting up Python, Jupyter Notebook and Google Colab
• Variables, data types and operators
• Conditions, loops and functions
• Lists, dictionaries, tuples and sets
• Working with files and libraries
• Mini-project: automate a simple data task

Module 3: Data Handling & Visualisation

• NumPy arrays and operations
• Data cleaning and preparation with Pandas
• Handling missing values and outliers
• Exploratory data analysis (EDA)
• Charts with Matplotlib and Seaborn
• Lab: analyse a real UAE business dataset

Module 4: Maths & Statistics Essentials for AI

• Descriptive statistics made simple
• Probability basics
• Linear algebra concepts used in AI
• Correlation, distributions and sampling
• Intuition behind gradient descent

Module 5: Machine Learning

• Supervised vs unsupervised learning
• Linear and logistic regression
• Decision trees, random forest and XGBoost (intro)
• K-means clustering
• Train/test split, cross-validation and model evaluation
• Feature engineering and overfitting
• Project: predict house prices / customer churn

Module 6: Deep Learning & Neural Networks

• How neural networks work
• Activation functions, loss and optimisers
• Building models with TensorFlow and Keras
• Introduction to PyTorch
• Improving model performance

Module 7: Computer Vision

• Image processing with OpenCV
• Convolutional Neural Networks (CNNs)
• Transfer learning with pre-trained models
• Object detection concepts
• Project: image classification model

Module 8: Natural Language Processing (NLP)

• Text cleaning and tokenisation
• Sentiment analysis
• Word embeddings
• Transformers and Hugging Face
• Project: customer review sentiment analyser

Module 9: Generative AI, LLMs & Prompt Engineering

• How large language models (LLMs) work
• Working with ChatGPT, Claude, Gemini and Copilot
• Prompt engineering frameworks and techniques
• AI for content, research, reports and presentations
• AI image generation tools
• Retrieval-Augmented Generation (RAG) — concept overview
• Limitations, hallucinations and fact-checking

Module 10: AI Automation & No-Code AI Tools

• Workflow automation with Zapier, Make and n8n
• Microsoft Copilot and Power Automate for office work
• AI in Excel, email and reporting
• Building AI agents and assistants (no-code)
• Lab: automate a real business workflow

Module 11: Building & Deploying AI Applications

• Using AI APIs (e.g. OpenAI API)
• Building a chatbot with LangChain
• Creating a web app with Streamlit
• Version control and portfolio with GitHub
• Deploying your app online

Module 12: AI Ethics, Governance & the UAE AI Landscape

• Responsible AI: bias, fairness and transparency
• Data privacy and the UAE Personal Data Protection Law (PDPL)
• UAE National Strategy for Artificial Intelligence 2031
• AI risk management in organisations
• The future of AI and emerging careers

Module 13: Capstone Project

• Choose a real business problem (marketing, finance, HR, retail, logistics, etc.)
• Collect and prepare data
• Build an ML or Generative AI solution
• Present findings and a working demo to the trainer panel