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Free Download NLP to LLMs Build the Understanding That Lasts
Published 8/2026
Created by OfferLab Courses
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 30 Lectures ( 2h 55m ) | Size: 859 MB
Attention traced by hand, LoRA written from scratch, RAG and evals measured. No GPU, no paid API, ever.
What you'll learn
Frame any text problem as a standard task type, and ship a TF-IDF baseline that earns its keep Trace one attention head by hand, with real shapes, until the mechanism stops being a metaphor Diagram a full transformer block from memory: multi-head, feed-forward, residuals, positions Narrate the GPT lineage as a series of specific bets, and explain why chat models behave unlike base models Control generation on purpose with temperature and top-p, and get reliable JSON and tool calls Estimate and cut the cost of an LLM feature before you build it, using context and latency budgets Build a RAG system and debug retrieval with recall checks rather than vibes Run a real LoRA fine-tune on a laptop CPU, verify the gain, and defend the build decision with your own numbersRequirements
Comfortable Python (you can read a class and a loop) Some exposure to machine learning basics helps but is not required No GPU, no paid API key, no cloud account. Every lab runs on a laptop CPUDescription
This course contains the use of artificial intelligence.
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. Same task, classical approach against the LLM approach, measured on cost and quality, and you write the decision memo. That memo is the artifact you show an employer, and it is worth more than another notebook with a high accuracy number in it.
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.
Who this course is for
Engineers and data scientists who use LLMs daily and want to know what is actually happening ML practitioners who learned the classical stack and skipped the transformer era Anyone who can call an API but cannot yet say when to fine-tune instead Not for: people who want a no-code prompt-tips course, or researchers who need paper-level depthHomepage
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