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.

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.

I like this specialization in general. However, this third one has too many overlapping contents and some videos are not that useful. Just personal opinion.

Homework is lacking. It is too easy to pass. I feel like the programming task or homework task fell short. The lectures were good but too little practice.

Good non-technical materials, but short enough to be incorporated into other courses. Some aspects feel subjective. Many typos/minor mistakes in quizzes

Was really enthousiastic about the first two courses in the specialization, the third however felt a bit like going back a step in level of advancement.

A different course for only two weeks of content? This is nuts. I waited for 15 days for financial aid to be approved and I completed it within 5 days.

Only interesting if you don't have much experience with machine learning; Might or might not be great if you are a novice, though - hard to say for me.

There was some very valuable material. However, I think some of the videos could have been prepared a little bit better and could do with more editing

Course was less interesting than the other parts. Also very negative that the last part of the 5-part specialization is taking ages to be finalized.

Good content, but could definitely benefit from a more fleshed out problem to solve. The content beg for a larger concrete coding exercice project.

It's easy and more simple than the others in specialization. Can be more deeper into ML project organization management. It's ok, could be better!

This course did not contain programming assignments, only quizzes, and was thus considerably less useful, even though the knowledge was important.

Quite some questions are confusing and some are not correct itself. and this course is more concept based, didn't actually get to program a lot.

Contains some useful tips, but they are a bit too diluted - I feel like it could have lasted much less and still conveyed the same information.

This course provides some interesting insights into how to approach machine learning projects, but feels a little light on substance at times.