Available courses

Data Structures & Algorithms

Arrays, hashing, trees, graphs and the algorithmic patterns behind them.

Retrieval-Augmented Generation (RAG): Design & Practice

Chunking, embeddings, vector search, reranking, grounded generation and citation discipline.

Foundations of ML & Python

Supervised learning, model evaluation, and the Python numerical stack — the prerequisite spine for later AI subjects.

Generative AI & LLMs

Transformers, prompting, retrieval-augmented generation, agents and tool use, and the evaluation of language-model systems.