The course was just a bunch of tips and suggestions. Yes, they are useful, but given the empirical nature of machine learning I would expect those tips to be accompanied by practical applications and homework.

I expected more. The videos were a little long and repetitive. The content was important, though. Maybe the course materials could be squeezed into one week and combined with the previous deep learning course.

THe course was challenging and had valuable inputs. But it would be even more wonderful if we got to work on some portion of the case studies as a capstone project at the very least. Else Its a 5 star course.

Good for the numerous hints about practical issues such as different distributions on train/dev/set. Very bad for the lack of hands-on assignments. Good practical advices but no occasion to see them working!

The quality of the course is not up to par with the other courses in the specialization. There is very little content and it is gone through too slowly. There are also more bugs and errors in the exercises.

Most of the materials in this course is tedious and have already been taught in previous courses. But I suggest the Transfer Learning and Multi-task Learning part, as well as the end-to-end learning part.

The course was misleading on what homework there was (machine learning flight simulation?) There was no homework. and the lectures were pretty bland compared to other courses in this specialty.

Transfer Learning and Multi-Task learning discussed in the course would greatly benefit from having programming assignments where people can play around with the data and learn confidently.

Good information about how to structure projects and how to boost performance. Not very hands-on however. Fits in well with the Specialization though as a break before CNN's and sequences.

Not as helpful... just a few suggestions and ideas... but there's no great application of the information learned here like a walk-through project or something with code, that's graded.

less useful than previous courses.

Would appreciate to go much deeper in directions like CNN, RNN, RL and review Unsupervised Learning (which was too light, ... no mention about RBM)

It was very theoretical and subjective.

It would be useful if the learner has some more experience in DNN than currently expected.

But I definitely enjoyed 2nd week of the course.

Interesting content, but lacking in real work. As with all of the deeplearning.ai courses thus far, the multiple choice questions are frequently ambiguous and poorly worded.

The information is really basic, most of it is self explanatory. This shouldn't be a course on its own, rather maybe a week/half weeks worth of material in another course.

This course is too subjective. Andrew shares his experience in a structured way in the lecture. However, I feel that correct structuring decisions need to be brainstormed.

Many useful tips but it's hard to remember. I think they'll become useful when I start building these voice/image recognition systems since I'm terrible at memorization.

Seeing different practical use scenarios and adaptions is fine but it got pretty boring without a real application to tune. The Quizzes on the other hand were very good!

Although everything taught is relevant, it was too much theoretical. And some of the evaluation questions are not clear (well, at least for non native English speakers).

Generally useful skills, but the contents partially overlap with previous courses and the overall quality doesn't match the previous courses (eg poor video mastering).

The course was interesting, but in my opinion too theoretical. I preferred the first 2 courses with Python programming. I am now looking forward to the next 2 courses.

Even though some of the content is useful, I feel like this course should be merged with the second one. There is not new information enough to justify a new course.

Good material was presented in this course but there were a number of technical errors in the video recordings. If they were cleaned up this course would be perfect.

Topics are a bit vague, which is fine as the content is interesting and useful nonetheless, but perhaps exposition is too lengthy relative to the amount of content.

Gives good insights on how to work on a Machine Learning project yet. Provides some rule of thumbs for different hick-ups that may be encountered during a project.

even though there are great tips and advices, it does not justify an entire course and they can be mentioned in 3 videos so a lot of the videos were repetitive.