Courses tagged "machine-learning"

The linear algebra every ML practitioner needs — vectors, matrices, transformations, eigenvalues, and how these map directly to neural networks, embeddings, and dimensionality reduction.

Design apps on top of language models — embeddings, retrieval (RAG), chunking, evaluation, and what to ship with confidence.