"Intermediate" level in the sense that you perhaps need some basic understanding of machine learning, but this is definitely not a course that challenges you. You get a very high level conceptual explanation of basic concepts (including things like LoRA, RLHF), but definitely no specifics on the implementation level. The assignment "Labs" consists of executing pre-written code in notebooks, and seeing the result output. No coding of your own, and typically just making function calls to Huggingface libraries, but not actually seeing how the algorithms are implemented.

Lots of theory with very little practice. You will not walk away from this course feeling confident that you know how to code any of it. The labs that are offered do not teach you much either. The instructors just tell you to not worry about how it works, and that it just works. The instructors need to add a whole lot more practice code to get you practicing the theory they teach. At this point, I am looking for where I can find that information because theory without practice is pointless.

The course material was all pretty superficial; the lectures never really delve into the nitty gritty details. The labs require no coding, which is disappointing. That being said, it's a good overview of the current landscape. If you want to learn implementation and the way things work (the how rather than the what), you will probably be disappointed.

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.

There is a lot of content compressed in 3 weeks. I don't have the impressions the lab helped me gain real hands on experience with Generative AI LLMs. It's very useful to have an overview of the topic, but perhaps this course should have been split in a series of courses.

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 .

Assignments we just reading code, frequently with little motivating commentary. Videos tried to cover too much content for a three-week course. It's very dense, even compared to the deep learning specialization.

Covered lots of surface area and explained the concepts well. The labs worked, but all you do it execute existing code. Not great for learning. Supposedly 3 weeks of material, but easy to finish in a few days.

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