Thorough and well taught course on strategy in ML. Would have enjoyed some programming exercises, but the assignments served their purpose well.

This course was relatively short, and the quality of the materials (lecture videos, quiz text) was somewhat poorer than in the previous courses.

Good practical advice. I would have added something about agile development and possibly practical advice on NN architectures (depth and size).

Excellent content but the quizzes are badly done, many questions have several reasonable answers and very little feedback if we 'get it wrong'

The course would have been more interesting if we had some programming assignments. Hands on experience into some of these cases really help.

Unique course in the sense that teaches important topics that are rarely seen in the literature and are fundamental in designing AI projects.

Had some pretty great info for junior Project Managers, for some reason, it's also hiding some extremely important info about end-to-end DL.

Course is a little short, only 2 weeks and a quiz. I feel there could have been another week added, with another interesting case study ,

Some of the videos weren't of good quality. Also, I was expecting doing a real project not to make decisions based on different scenarios.

This is an important course for practical purpose. It tells us about the methods to use for improving your model by doing error analysis.

Good course overall, would have liked to have the in-depth programming assignments though, those really made the other courses stand out.

Learned various ways to structure ML projects in industry.

It would have been great to have few programming assignments included as well.

Some tips are very useful for practitioners but the same information is repeated over and over again that makes the course quite boring.

A small course with very effective tips and tricks to figure out how to start and proceed further while building a project effectively.

I think this lecture is very useful when we make our own ML system.

Also, it has many examples about errors we can usually meet in real.

Useful, practical material. I probably underappreciate the importance of someone (especially of Dr. Ng's stature) covering this for us.

Very practical lessons in this module that should make you and your team more efficient in implementing deep learning on real problems.

another great Andrew Ng course. This one gives practical insights in how to go about making your deep neural networks perform better.

I really found the pilot training quizzes are great and very helpful, but some questions one can debate if has the right answer or not

Quizzes could be refined since some of the questions are really confusing & need weird pre-requisite knowledge about human physiology.

It was an interesting course for sure, but it was a bit stretched and the notions explained could be compressed in a much shorter one.

I appreciate the review and hints on structuring ML projects. Just seemed a little lacking on the meat and potatoes of real practice.

Now I know what is Machine learning and its parts eg deep learning. The curse cleared the basic structure for machine learning to me.

Interesting to understand how to manage a problem during a ML project, really good trick and tip! Thanks Andrew and deep learning.ai!

It would be more useful to give explanation why an answer is correct and others are wrong, such details enhance our learning so much.