LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
Why it matters
If you want to go from zero to deploying production LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → applications, this course provides a structured three-part path — fundamentals, model science, and engineering — with runnable Colab notebooks for fine-tuningTaking a trained model and training it a bit more on your own examples so it gets better at one specific job.Full definition → and quantizing real models like Llama and Mistral. Unlike most LLM tutorials that stop at API calls, it covers the full stack including model merging, quantizationShrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.Full definition → techniques, and deployment patterns that are actually used in production.
