Rama Ramakrishnan teaches lecture seven of MIT's Hands-On Deep Learning course (15.773, Spring 2024), covering how transformer models process natural language. He builds the explanation around an airline travel example, using it to walk through attention mechanisms and how transformers weigh relationships between words in a sequence rather than processing them strictly in order. The session runs 77 minutes and fits into the course's broader arc on modern deep learning architectures, following earlier lectures on foundational neural network concepts. Ramakrishnan keeps the focus practical and applied, aimed at students who want to understand why transformers replaced earlier sequence models like RNNs for language tasks, using the concrete example to ground otherwise abstract architecture details in something students can visualize step by step.