Courses

Free, in-depth courses written to be read from start to finish, with diagrams, worked reasoning and questions in every chapter.

• Advanced RAG Masterclass — Take Retrieval-Augmented Generation from a demo to a production system: chunking and embeddings, vector indexes at scale, hybrid search, reranking and query transformation, rigorous evaluation, keeping knowledge fresh, agentic and graph-based RAG, memory, and multimodal documents.
• How to Crack the AI Engineer Interview — A map of what AI engineer and GenAI interviews actually test, and the study path through it: machine learning foundations, transformer internals, efficiency, alignment and fine-tuning, prompting and retrieval, evaluation, and the failure modes every interviewer asks about.
• Deep Learning: Fundamentals to Advanced Concepts — Learn the complete deep learning with this course starting from basics to current day attention networks.
• A Brief History of Deep Learning — From the first look at a neuron in 1871 to today's large language models: the ideas, the hype, the winters and the breakthroughs, with simulators to try the key ideas yourself.
• Introduction to Artificial Intelligence — Get a gist of what AI is as a beginner student.
• Large Language Models: From Transformers to Frontier Models — Build a modern large language model from the ground up: tokens and embeddings, the attention mechanism, positional encodings, and the efficiency and scaling ideas behind today's frontier systems — KV caching, grouped-query and latent attention, mixture-of-experts, multi-token prediction and low-precision training.
• Machine Learning Techniques — The core algorithms of classical machine learning, built from first principles: PCA and kernel PCA, k-means, maximum likelihood, Gaussian mixtures and EM, linear, ridge and lasso regression, kNN and decision trees, naive Bayes, the perceptron, logistic regression, support vector machines, bagging, boosting and a first look at neural networks.