Compared to previous two courses in specialization this one is far worse - it is more of excursion into used methods than actual learning using any of mentioned methods in enough detail to be able to do meaningful analyses.

Feels like everything is solved using a caret package, while the back-end theory is only slightly touched. By using a single line command solver, student may lack the foundation for harder problems in the real world.

Seems like a lot to pack into 4 -weeks. Should really be named introductory machine learning. Needs more depth and better development of the intuitions associated to each algorithm class to match the expectations.

The material covered was good and informative, the reference material was nice. But the video leactures themselves were lacking in many respects. The videos covered only a bare minimum and could have been longer.

I like initial courses like Exploratory Data Analysis but later on it got harder to follow the lectures. A lot of topics were just rushed through and little effort was made to make them engaging or interesting.

Very interesting subject area, I think there is simply too much to cram into one course. Should consider spliting the subject into 2 courese or simply concentrate on only 1 or 2 main areas (e.g. cla

The videos are really tutorials on R functions for machine learning and data wrangling. A good substitute for "Machine Learning" by Andrew Ng in terms of managing data sets and exploratory analysis.

Content somewhat outdated. Referenced packages don't always work in current version of R. Material can be better explained with more detailed discussion of examples rather than theory.

This course needs swirl assignments. I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

There was some good material in here, but it was rushed and is deserving of a much longer course - especially compared to some of the other modules in this course.

the lack of assisted practices made it harder to digest the contents and methodologies.

strongly suggest to develop some practice problems with explanations.

Good in introducing caret package and getting some experience in running algorithms. Was expecting more in-depth discussion about the methods though.

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

Rather good overview. The contents could dig deeper into each subject, and it would improve the course a lot if some exercises in Swirl were added.

I didn't enjoy the supporting materials and the quizzes weren't very interesting. The final project was fine.

The subject is super interesting.

Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.

A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

Good course for learning the basics of the caret package. However, it is not a good course for learning machine learning.

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.