LLM Fine Tuning on OpenAI (Udemy.com)

Course covers it's scope well and the instructor is engaging and speaks clearly. There have been quite a few updates to the UI on the openai side, but the information is still relevant with respect to data formatting and the underlying processes. Would recommend, but should be aware that further updates beyond section 3 [openai gui] have occurred.

Evaluating this course for company use. Found it very helpful as an introduction to fine-tuning LLMs. Recognizing it is difficult to keep up with versioning and interface changes, I did find it a bit challenging to ensure I navigated to the right locations on newer OpenAI interface. Overall, great course.

This course offers a clear and informative introduction to fine-tuning LLMs using OpenAI tools, with practical insights and troubleshooting tips. I recommend it to anyone ready to move beyond prompt engineering, though adding free exercises or quizzes would further enhance the learning experience.

Gives good high level picture of how to fine tune gpt and to measure the quality. What I would add is explaining how to read the quality benchmarks provided by openAI. We can see the plot with base model and fine tuned model values, but it's not clear what these values actually mean and how to interpret them.

Learnt quite a lot from this short course. Very good presentation, as always with this instructor. There were some small issues with the notebook and video caused by OpenAI modifying its Python API. Nothing big though - easy to solve and the instructor mentioned all of the changes during the video I think

Good intro walkthrough of finetuning with openai api. Although many use cases are possible, main focus of the example here is on fine tuning the LLM for text classification...exactly what I was looking for. And, the code presented all worked - not always the case with changes at openai.

I would love more 'theory' or explanations, and to compare to other methods on improving the performance of LLM's. Although this could be taken as an isolated course, explaining where it lies in the context of other methods would be relevant for the new apprentices like me.

As always, Jose has created another great course. I feel like Jose will probably add more content to this one down the line as it is one of his shorter courses, but if you are looking at learning specific fine tuning processes, this is a good one.

Has a lot of potential and some good information, but, as other reviewers have pointed out, it falls a bit short on more examples or a deeper example with more significant improvements. I hope this course gets more updates in the future and I can update this review. Still a very nice and useful course, just stopped a little bit short of going past the very basics.

The course takes a very simple use case, so it's only useful for having a panoramic idea on what a fine tuning process works. I would have appreciated some more complex use case. For example, what if I have leads stored in a MySQL database and want my model to answer questions about them? Mr. Portilla is a great teacher anyway, I've loved a lot of his courses