good

A nice and informative course. The only negative side were the problems with the automatic evaluation of the R assignment. In my opinion, the question should have been automatically removed and/or all submittions reevaluated, or all students should have been notified about the need for manual resubmission. As it was, some (like myself) were left with fewer points that they should have received just because they did not check the discussion forums every day (mainly because of other obligations).

Excellent crash course in machine learning and introduction to the kaggle data science competitions. However, the grading system had bugs and was unable to accept two answers as correct making it very frustrating. The grader was finally fixed so next round of this course should be a better experience.

This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.

I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.

Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.

The entire course is an overview! This course will be a revision if you already know the concepts.

Very good approach to each method; the assignments are a good test for the topics.

Very interesting course. It would be useful to download slide used during videos.

Great Course but the assigment don't show the understanding of the course

More dynamic visualisation please, and it will be 5*.

Was expecting more to learn on stats and R.

rather nice course. learn R before joining

good course material!

very useful

The lectures in this course were very good but I would have preferred much, much more homework to practice the concepts covered in the lectures.

Also, I was somewhat disappointed when a certain issue with the course that I asked about in the forums was never addressed by the course staff. Of course, I could have been wrong about it but, but based on the response from other students I was not the only one having this problem.

Great overview of many models and techniques, but very high level. Would have greatly benefited from links to resources to learn more about all the subjects. This course leaves students with only basic knowledge of the subject matter, which is fine considering the course timeline. But, for those who want to explore further please recommend sources of additional reading and research.

This course was in some ways a disappointment. Although the lectures were intriguing and clear, I felt like the assignments were essentially "Go and pick up R on your own," which was pretty frustrating.

I felt that topics were not treated in enough depth. It was a lot of topics to cover in a 4-week course.

I learned a lot from the kaggle competition.

Interesting course and specialization. A few inaccuracies need to be corrected so it can be properly pursued at its best.

Problems with vitual machine for R assigment. For peer review detail evaluation guidelines is need.

It's a decent course. Not as thorough as Analytics Edge or Machine Learning (by Andrew Ng).

Meh, if you want to really dive in predictive analytics go to other courses.

the "practical" part is not really existent

it gets on my nerve from 3rd Work onwards