While the course content and organization was great, I had issues in accessing the AWS labs (Week 2 and Week 3) where I couldn't execute the Python notebook steps after few steps and got stuck. When I tried to resume by restarting the terminal it said invalid authentication and could not complete the labs and had to do forceful submission.

The videos in the course were helpful, with the exception of the lab videos.

I found those simply regurgitated what was already in the lab notes.

The labs themselves were only partially helpful due to the high-level code being used.

I would have actually preferred to go a bit lower-level in implementing a few pieces.

I prefer assignments where I actually have to figure something out. Just running code is not an effective learning method. I paid for access to those "assignments" that offered nothing more than watching the video of the guy going through the assignments.

I think the course needs to have a better balance between the contents. For example, it spends tons of effort talking about different parameters in model inference, which is as simple as 1+1, but it touches very lightly regarding how transformer works.

The concepts thought were at very high level and the instructors were good in covering it all - however the downside is none of it is covered indepth due to which one may lose track of what exactly is being thought about .

The labs were just run the modules, there should have been assignments related to building and fine-tuning models, the labs could have been where we were asked to code rather than just run the blocks

This course helped me to learn the basics of fine-tuning and aligning LLMs. However, the Labs are simply demos rather than practices, and a bunch of technical detail is omitted.

Good overview but like too often on Coursera, the assignments are too easy. You don't need to write a single line of code to pass...

The lab parts do not make the student work nor present a challenge, they only make the student run through someone else's code.

(+) Excellent info, representation and organization

(-) The practical part is not good (some hands-on need to be added)

Week 1 and 2 are great but 3 isn't that exciting. I was expecting the course to be more technical.

Helpful introduction to LLMs but I wish we got the chance to go in-depth on implementation

A lot of issues with the labs. Contacted supported and waited for long but no resolution

Not explained as clearly as you would expect from an Andrew Ng course.

not very interactive, and the closed captions were often wrong

not challenging, shallow, wouldn't call it "intermediate"

Not very technical course, but gives quite an overview

Not too practical Just a broad view on the subjecgt

Theory was good, but labs should be more practical

its LLM 101 explaining intuitions behind it

need more practic labs and videos.

more hands on

Most of the course content was quite shallow, only skimming the surface of each topic. I felt that it was a good primer on LLMs and I'm glad I took the course, but if I could go back in time I'd simply audit it. The labs and quizzes add very little value and are in no way worth $50.

Poor explanations. Can't hold the attention of the student. Start with problems and solve them using Generative AI and gradually explain different ways and concepts.

This course is okay for non-technical/non-programmers for understanding the concepts.