Initially a bit hard to understand but repeating the session helped to grasp the concepts

Good course, but there are too many small topics in each week and no hands-on assignment.

Not quite sure, but there are some problems in exam. Hopely fix them as soon as possible.

A good theoretical course to help remember the nuances of how to structure a ML project.

It was a nice course, it can better if some demo codes are used as an example separately

Pro: useful practical suggestions;

Con: language used in quiz should be further polished.

"Chillout course", but "test-by-real-cases" is very exiting and very fine idea. Strong 4

I wish there was some practice assignments related to the concepts learnt in this course

Info about the approach in applying DL/ML concepts to various scenarios were explained.

Interesting course, but all this could have been summarised in the first course itself!

This course is not so well organized as the previous two. Everything else is very good!

Very valuable insight in real world usage of DL and guidance in approaching DL projects

It would be even better if there are more quiz and assignments made in ipython notebook

Not a necessary course in my opinion. His other courses in this series is much better.

The theory from videos is awesome but the tests have less specific problem description

This gives subtle but important knowledge

about training with different distributions.

The pacing was a little off, and perhaps some code would help. Otherwise, pretty good.

Useful and short course, but with a lot of information repetition. Could be optimized.

Very helpful in understanding the way the machine learning projects should be managed.

Very useful, but some questions were misleading or were not discussed until next week.

The material is good, but too short. It could have been compressed in another course.

Good module to learn about how to structure the ML project and getting insights of it

I thought there would be more about project management specifics of machine learning.

I feel like this course should be the last course in the sequence, not the third one.

This class gives an excellent overview of how to optimize a machine learning project.