It is rich in information

It is a great course.

GREAT COURSE!

Good

Good projects are an excellent starting point for beginners. However, they should focus more on explaining the mathematics involved, such as variance, covariance, and distance functions. It's important to explain the roles of these functions in K-Means clustering and how to use them effectively.

Level of difficulty of assessments is quite unbalanced. End of week summative assessments are merely a compilation of the practice quizzes within the week, does not really test my understanding. For the programming assignments, the first one was quite trivial while the rest were challenging.

The course was useful for me until the fifth week. I think the videos of the last week are very useless and uninformative, so disappointed about the waste of time. I would advise the last lecturer to work on his material, and not just read the assignment

It teaches how to do it, but the editing could be better and the evaluation of the model could be deeper. This course suits better absolute beginners.

All the statistics and k-Means algorithms are well explained, but there is much missing guidance on how to conduct the final project.

Mathematics taught is very abstract. Not many practice examples and linkage to practical side. Not much guidance on guided projects.

Good introduction to k-means clustering using Python. Easy for follow.

Good course overall.

very awful course with terrible course agenda.

useless peer-revirewd assignments and complete waste of time

purchase certificate? u gotta be kidding me!

horrible course, bad instructors, watch all videos for full length it's

Instructions were awful, and the peer review is useless