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!

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

Content is too easy

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments.

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

This course probably deserves 3-4 stars in a better, maintained form, but the entire specialization is not maintained, the lectures have no production values. Basically, it's a money pit that Coursera is keeping up cynically. It's a real shame because the syllabus correctly addresses a gap in most data scientists' skills.