AI / ML

Understand, use, and govern AI-driven components in production systems.

• Beginner: Foundations — math for ML, data pipelines, model lifecycle
• Beginner: Tools — Python ecosystem, notebooks and visualization
• Intermediate: Supervised learning — feature engineering, hyperparameter tuning
• Intermediate: MLOps — monitoring, drift, deployment patterns
• Advanced: Architectures — graph neural networks, reinforcement learning
• Advanced: Research in practice — ethics and responsibility