NLP to LLMs: Build The Understanding That Lasts

NLP to LLMs: Build the Understanding That Lasts
Published 8/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch

You can call an LLM API today. The question this course answers is what to do when the API is not the right answer.
Most LLM courses teach you to use a product. This one rebuilds the understanding underneath it, in the order the ideas actually developed: text as sparse counts, then dense meaning, then attention, then the models that scale it, then everything we have learned about making them behave.
The labs are the point, and they all run on your laptop.
You will trace self-attention by hand on a toy example, with shapes, before you ever see the library call. The LoRA layer in module 7 is written directly against torch rather than pulled from a package, because the whole point is to see the low-rank update. The ROUGE implementation in module 8 is thirty lines of Counter. Nothing here is a black box you are asked to trust.
Every number in this course was measured, not estimated.
The module 7 fine-tune takes 233 seconds and the capstone’s four-system comparison about six minutes, both on one laptop CPU with sixteen torch threads. Timings are machine specific and labelled that way in the lessons; accuracies reproduce at the stated seeds.
The capstone makes you choose and defend.
The course also covers what most curricula skip: where models fail systematically, how to stand up a minimal eval before shipping, and how to threat-model prompt injection and leakage at practitioner level.
Module 1 is free. Watch it and you will know whether this is the right depth for you.
The other three OfferLab courses:
Cracking the US Data Science & ML Interview · The US Tech Interview: Behavioral, Communication, Offer · The ML System Design Interview. Each stands on its own; take them in whatever order fits what you are preparing for.

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