Most of the material was quite useful, but some was, perhaps, too obvious. Also, some things were discussed too thoroughly, and, in my opinion, that was a waste of time.

Would be great to obtain more concrete information.

For example, instead of "requires much more training data" to obtain "requires ~1'000'000 samples instead of ~100'000"

Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.

I wish there was more examples, visualization and depiction of work with referral to papers or experiment here. or perhaps a bit of project management, data management.

Enjoyable, but felt a little less challenging and more hastily assembled. Regardless, the material is valuable and as always, a pleasure to be instructed by Andrew Ng.

The course material is relevant and useful, however, I agree with other reviewers that these 2 weeks should rather be a 1-2 weeks addition to one of the other courses.

Quick course. Worth taking because gives some practical guidance on what avenues to pursue when finding a optimal model (which takes into account human time required)

The first week is too long with repetitive materials. The second week is very interesting. However, I wish the course was designed such that it required some coding.

Great course, great professor .. the only issue is that I feel sleepy every time I watch the videos :), it's some how single tone. Also the audio could be improved.

It is always important to learn above the problem-solving methods and tools. This course teaches the complete diagnosis methodologies for Machine Learning problems

Overall, very informative, however I think the content of this course could be divided between the first and the second course.More assignments would've been nice.

Lots of practical advice and ideas on how to work on actual projects and things to look out for. Great stuff. Wish it had a few programming exercises or a project.

Useful introduction to meta-level principles of machine learning process management, but not quite as groundbreaking or well-instructed as the previous two courses

A real time project or programming assignment could improve our confidence level.

All of these courses if it had readable material along with video, it'd be great.

Hope to have coding practices for the second week's materials.

Anyway, the current course is already very helpful. Thanks to Andrew and all staff behind the scene!

Content is good, but a bit thin... This course makes sense as part of the deep learning specialisation, even if this is a bit too short to be a course of its own.

A bit less interesting than the others I think. To me the whole first week was full of obvious stuff. The second week, however, was very interesting and helpful.

There was lot of theory and probably not one of my strengths. However the content is very useful for bringing some structure to machine learning problem solving.

This course could have just been an extra week or two of course 2. It doesn't have the depth of the others, although it is very practical and I like the content

Good advice on how to work on a machine learning project from the ground up. Tho most of the material is already covered in Ng's Machine Learning Yearning book.

Nice to see a course on machine learning about the 'other stuff' around machine learning. However, links didn't work half the time and it was a bit unpolished.

Some of the lectures feel quite lengthy and repeat stuff. It seems to be easily possible to condense into one week which could be added to the previous course.

a lot of topics are covered in machine learning course, but this has an upgrade to input from previous course due to changes has happened in AI in last years.

Valuable tips to apply in machine learning projects. I'd like to have some programming assignment that gives opportunity to practice some of those techniques.

Es muy bueno el curso lo recomiendo si se desea tener un conocimiento general sobre los aspectos a considerar en las etapas del desarrollo de proyectos de ML.