ML Interview Notes

A sequential, build-from-scratch course on the Transformer architecture, from "why did we abandon RNNs?" through to the exact configuration choices in production models shipping in 2026.

Mathematical foundations essential for Machine Learning including linear algebra, calculus, probability, and optimization.

Essential Python libraries and frameworks for Machine Learning development and deployment.

Core Machine Learning concepts, algorithms, model evaluation, and best practices.

Neural networks, deep architectures, optimization techniques, and advanced deep learning concepts.

Natural Language Processing techniques, models, and applications for text and speech processing.