Introduction to Artificial Intelligence
What AI is, what it can and cannot do, and where you already meet it every day.
Artificial Intelligence
Understand how AI works, use it well and responsibly, and build a small project of your own.
Machine Learning Basics
Understand how machines learn from examples: supervised, unsupervised and reinforcement learning.
Data for AI
Collect, clean and label data, because a model is only as good as its data.
Neural Networks and Deep Learning
See how layers of simple units learn patterns in images, sound and text.
Generative AI and Large Language Models
How language and image models generate content, and why they sometimes make mistakes.
Prompt Engineering
Write clear instructions that get useful, accurate answers from AI assistants.
Computer Vision Basics
How computers recognise objects, faces and text in pictures and video.
AI Ethics and Responsible Use
Bias, privacy, transparency and accountability when using or building AI.
AI Tools for Work and Study
Use AI tools to summarise, translate, research and organise your work safely.
Build a Small AI Project
Plan, build and present a small project from problem definition to evaluation.
AI Agents and Automation
Understand how AI agents plan, use tools and act on multi-step tasks.
Natural Language Processing Basics
See how computers break down, represent and analyse human language.
Recommendation Systems
Learn how apps suggest products, videos and content you might like.
AI in Robotics
Explore how AI helps robots sense, plan and move in the physical world.
AI Applications in Healthcare
See how AI supports diagnosis, imaging and healthcare research responsibly.
AI for Business Decisions
Identify where AI genuinely helps a business, and where it does not.
Math Foundations for AI
Build the intuition behind the vectors, probability and gradients AI relies on.
Python Libraries for Machine Learning
Get oriented with the main Python tools used to build ML models.
Evaluating and Improving Models
Measure whether a model is actually good, and know how to improve it.
The Future of AI and Careers
Map AI-related career paths and where the field may be heading.
How Large Language Models Work
Look under the hood of transformers, attention and next-token prediction.
Retrieval-Augmented Generation (RAG)
Ground AI answers in real documents instead of memory alone.
Fine-Tuning and Customising Models
Adapt a general model to a specific task or domain.
Multimodal AI: Text, Image and Audio
See how models combine text, images and audio in one system.
AI Safety and Security Basics
Understand the main risks that come with deploying AI systems.
Building AI Products and Startups
Go from a model to a real product people can use.
Edge AI and On-Device Models
Run AI directly on phones and small devices instead of the cloud.
Data Engineering for AI
Build the pipelines that feed clean, reliable data to AI systems.
AI Capstone: End-to-End Product
Design, build and pitch a complete AI-powered product from idea to demo.
Building Conversational AI
Design chatbots and assistants that handle real conversations well.
History and Milestones of AI
Trace the main eras of AI and explain why each wave of excitement rose and faded.
AI in Daily Life and the Arab World
Spot the AI around you and describe how it is used in Arab countries.
Search and Recommendation Systems
Explain how search engines rank results and how recommendations are personalised.
AI for Writing and Translation
Use AI to draft, edit and translate text, including Arabic, while keeping quality and your own voice.
Critical Evaluation of AI Output
Check AI answers for accuracy, sources, bias and fitness for your purpose before you rely on them.
Python for AI: Tools and Notebooks
Set up a notebook and use core Python libraries to load, explore and plot data.
Feature Engineering and Data Preparation
Turn raw data into useful model inputs by encoding, scaling and creating new features.
Decision Trees and Ensembles
Build and read a decision tree and explain why combining many trees often works better.
AI for Images, Audio and Video
Create and edit media with AI responsibly, and recognise synthetic or manipulated content.
AI in Education and Healthcare
Evaluate realistic uses of AI in classrooms and clinics, including benefits, risks and safeguards.
Transformers and Attention
Explain conceptually how attention lets a transformer weigh context and why it scales to large language models.
Prompting, RAG or Fine-Tuning?
Choose between prompting, retrieval-augmented generation and fine-tuning for a given problem.
MLOps: Deploying and Monitoring Models
Describe the path from notebook to production and how to monitor a model after launch.
AI Safety, Alignment and Security
Recognise the main failure and attack types against AI systems and apply defensive practices.
AI Governance, Law and Privacy
Apply governance principles, privacy rules and risk assessment to an AI project.
Evaluating and Benchmarking AI Systems
Design a fair evaluation for an AI system and read benchmark claims critically.