Material was very interesting but was covered at a very high level and a lot of additional learning was required.

Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python

Instructor rushes the course and does not explain much in the same level of details as respective quiz requires

All the formulas and code in slides are too abstract. If can be more charts to interpret that will be better.

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

A fine introduction, but there are much more engaging and better quality courses out there...

If you have taken Andrew Ng's machine learning class, it's not necessary to take this one.

I think it was a very fast course and I feel more real examples would have been useful,

Poor supporting material in a course that tries to cover a lot in a very limited time.

this course seemed too rushed for me, too little content for such a extense subject

more needs to be taught in class. what is taught is not sufficient for quizzes.

Overally course is good. But weekly programming assignments will be great.

Too different for beginners but not deep enough for ones already know R.

Quizzes are useful exercises but need to do a lot of self studying.

mentorship was great, but the video lectures were almost useless.

The topic is too big, for one course from my point of view.

In my opinion, this course needs a few extra exercises.

Fair but assignments are not very well explained

Good enough to open up mind of researcher

I need study more this course

I miss Swirl

too easy

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I really enjoyed the content of the course. I already knew a fair amount about machine learning but I learned a lot more than I thought I would. Most contents of weeks 3 and 4 - decision trees and random forests, bagging and boosting, linear discriminant analysis and naive Bayes, forecasting and unsupervised predictions - were my favourite topics in this course.

The biggest disappointment in this course for me were the outdated quizzes. I worked really hard through this course and most of the Data Science specialisation. But the quizzes are set up for older versions of R and some of its packages, so the results are completely different from what I got most of the time. I found this extremely frustrating and disheartening and had to repeat the quizzes several times. I do realise that most quizzes enumerate at the beginning the versions they are using, but there is no mention of how one goes about to set that up in R. On top of that, given that I rarely passed the quiz on the first try my Skill Tracking score dropped considerably, undermining weeks and weeks of hard work.

Unfortunately, this tainted my view of this course and I would advise the course organisers to update it as soon as possible.

Had big expectations for this one... really one of the ones to look forward to after working through the beginning of the specialization, but for some reason, it seemed any prof or even TA interaction was absent this time around like in none of the other specialization coursed to date. Bugs in the new interface and quizzes weren't really addressed. Couldn't even get an official response about the apparent removal of Distinction-level now (which I'd been working to get in all specialization courses and now seems no longer an option). Still interesting content. As a "free" course, it's still really valuable. As one of the people that paid for this and all others in this specialization, this is the one I felt didn't return as much value to justify the payment with no "official" course staff seeming to be involved this round.