Dec 23, 2016
Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.
Feb 08, 2016
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
by Roberto S•
Jun 13, 2017
Very good approach to each method; the assignments are a good test for the topics.
by William L K•
Jun 06, 2017
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.
by Nico G•
Dec 22, 2015
Very interesting course. It would be useful to download slide used during videos.
by Ashish S•
Jun 29, 2019
Was expecting more to learn on stats and R.
by Antonio P L•
Jan 08, 2016
Great Course but the assigment don't show the understanding of the course
by Nathaniel E•
Jun 08, 2017
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.
by Kairsten F•
Oct 26, 2016
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.
by Harald B•
Mar 17, 2016
the "practical" part is not really existent
by Solvita B•
Mar 17, 2016
Problems with vitual machine for R assigment. For peer review detail evaluation guidelines is need.
by Sambed A•
Dec 25, 2015
It's a decent course. Not as thorough as Analytics Edge or Machine Learning (by Andrew Ng).
by Faisal G•
Nov 21, 2016
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.
by Guido T•
Jan 21, 2016
Interesting course and specialization. A few inaccuracies need to be corrected so it can be properly pursued at its best.
by Lucas S•
Mar 15, 2017
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.
by Benjamin F•
Feb 04, 2018
Meh, if you want to really dive in predictive analytics go to other courses.
by Praketa S•
Nov 07, 2016
it gets on my nerve from 3rd Work onwards
by Andrew T•
Jan 12, 2016
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.
by Robert H S J•
Feb 15, 2016
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.
by Sasa L•
Jul 17, 2016
Content is too easy
by Lei Z•
Mar 22, 2017
The course is good. But it does not has lecture slides that is better for students to understand.
by Andre J•
Jun 21, 2016
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).
by Ben K•
May 27, 2016
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.
by Jana E•
Dec 07, 2017
Same as before, subjects are quite interesting, but the video material is of quite low quality.
by Sajit K•
Feb 13, 2016
Unrelated and incohesive lectures. Disappointed. Lots of random topics talked about .but nothing in depth.
by Qianfan W•
May 09, 2016
Do not like the slides and the way it is explained. Compared with other ML courses on cousera, this one makes me feel that it is more like a handbook/dictionary instead of a tutorial to teach students. If you already know it, it would help you refresh the mind. Otherwise, you might find it is just to show off how how complex and mysterious is the data science.
by Jonas C•
Apr 19, 2017
The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.