Very Good

Thank You

Loved it!

Awesome!

Amazing!

helpful

Perfect

useful

thanks

superb

Brutal

Greate

teert

Nice!

goodd

Super

great

شكرا

good

nice

good

good

good

All the instructors were good and delivery was mostly excellent, however, the course was a bit too short can be improved in several ways. There were very few quizes in the video lectures and the ones that were present, were too easy or obvious (does not require much thinking). There should be good, quality quizes in most video lessons similar to the OG ML course by Andrew Ng. The inline quizes in videos help "reinforce" the learning in humans. This is proven by the research yet to be carried out :D Another aspect that I did not like was the jupyter notebooks to run excercises, all solutions were already provided and it does not help in learning the concepts if all we have to do is to press Shift+Enter and merely observe code and results. Actual learning requires some trail and error as part of the exercises, once again the OG ML course by Andrew Ng did a good job of accomplishing this with Octave exercises.

A delightful and very up-to-date (most of the references have been published in the last 2 years) overview of LLMs with hands-on lab sessions in Python. Prompt engineering, zero/one/few-shot inference, instruction fine tuning (FT), parameter-efficient FT (PEFT), Low-rank Adaptation (LoRA), RL from human feedback, program-aided language (PAL) models, retrieval augmented generation (RAG), etc, etc. In short, everything you need to know about the state-of-the-art in LLMs in 2023. There are a couple of things that disappointed me though. The first one is that, unlike other Coursera courses, there isn't any discussion forum to interchange ideas with other students or post questions. The second one is that there isn't any clear contact (either from the course's intructors or from Coursera) to ask questions regarding problems with the AWS platform when working on the labs.