AI

What you will learn

• Explain how supervised, unsupervised, and reinforcement learning work with concrete examples
• Build intuition for how neural networks learn by adjusting weights through gradient descent
• Evaluate AI system outputs using accuracy, precision, recall, F1, and confusion matrices
• Describe the transformer architecture and explain why attention mechanisms matter for LLMs
• Apply prompt engineering and retrieval-augmented generation patterns to real tasks
• Assess AI risks using the NIST AI RMF and EU AI Act risk classification

Single learning path

• Senior leaders, technical managers, AI practitioners, engineers, analysts, architects, and governance owners who need one shared path from first principles to expert AI judgement
• Professionals accountable for AI strategy, model selection, governance, platform decisions, security, evaluation, and regulatory trust
• Technical specialists who want deeper foundations across machine learning, deep learning, LLMs, RAG, agents, safety, MLOps, and executive decision-making

Prerequisites: None. The course starts from zero and builds one single path for every learner. Basic numeracy is helpful, and the course explains the mathematical ideas when they appear.