Twelve tracks.
/ one skill graph.

Build a solid understanding of artificial intelligence and machine learning from the ground up. Covers core concepts, algorithms, and hands-on NumPy exercises.

Dive deep into large language models: how they work, how they are trained, and how to evaluate and compare them.

Master the art and science of writing effective prompts. From fundamentals to advanced techniques and security.

Learn to build retrieval-augmented generation systems end to end: from document processing to production deployment.

Design and build autonomous AI agents that reason, plan, use tools, and collaborate with other agents.

Ship AI to production with confidence. Covers API integration, cost optimization, observability, testing, and deployment patterns.

Build the mathematical intuition behind machine learning: linear algebra, calculus, probability, and information theory — all applied directly to ML concepts.

Bridge from classical AI to language models. Covers text preprocessing, word embeddings, sequence models, and the evolution to transformers.

Go beyond the basics: master CNNs, RNNs, GANs, training techniques, PyTorch, and transfer learning for production deep learning.

Learn how agents learn from interaction: MDPs, Q-learning, deep RL, and the path from game-playing to RLHF.

Ship ML models to production: experiment tracking, pipelines, containerization, CI/CD, and monitoring for the full ML lifecycle.

Build responsible AI: interpretability, fairness, adversarial robustness, governance, and alignment with human values.