Not quite as practical and informative as the first two courses in this specialization, but overall still quite enjoyable and helpful. Excited for the next course.

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.

Advanced course, as always well done :D

Anyway, something for me strange is that there are only quitz but not exercices in Python/Tensor Flow... This is a pity

I loved the lessons, they are very practical. The only thing that I think could be improved are some of the quizz questions are worded in a bit ambiguous way.

It's an useful course to give me how to analyze error and give me some advice how to apply transfer learning and multitask learning. Thank for your course.

I felt that this course was too slow for my liking. While I agree this helps a lot, But I watched all the videos at 1.25x the speed so that I dont get bored

It doesn't have coding but it teaches thing that will really help in real projects when we will need to decide best approach based on multiple data points.

The courses of this two weeks make me confused because i have't built a machine learning systems fully by myself yet , so i found those classes boring.0.0

Not as helpful or practical as earlier lessons, but nonetheless important high-level advice. Perhaps there was a better way to test knowledge than a quiz?

Great course, like andrew said this course will make u more strategic toward machine learning projects and problem, and i would say mission accomplished!