This is such a cornerstone topic to the Data Science Specialization that I think it deserves a better designed and more polished curriculum. The subject is so extensive that it might be worthy to split the contents in two courses. Finally, I would like to suggest the authors of the course modeling the curriculum following the amazing treatment of the subject found in "Introduction to Statistical Learning" by Hastie, Tibshiriani et. al.

This is a good course for someone who has already done the previous courses in this specialization series.

It covers the most basic ideas in machine learning and expose you to work on real problems and learn by experience. if you are looking for more advanced in-depth courses, you need to take other courses as well.

Overall, lectures are in very fast pace and as a result they have several mistakes in them you should be careful about.

The course feels rushed. I understand teaching Machine Learning in 4 weeks is impossible, but then maybe the course needs to have a narrower yet deeper scope rather than throw at you many concepts without details. e.g. trees, random forests, bagging and boosting all in 10 minutes each? Impossible...

So, as opposed to creating machine learning intuition I feel the course became an R package code book.

Unfortunately, I didn't think this topic was as good as the other courses in the Specialisation. Quizzes often references aspect that haven't been discussed during the lessons, and the lessons itselves are often too high-level (although I reckon this is why the course is called "Practical", and we might need several courses to thorough fully understand how each algorithm works).

In my view the course was useful but not as good as the previus ones I followed in the specializacion (such as regression models and stat. inference).

The subject was too broad and there was no space to cover in detail all the algorithms. Also I think it's a bit out of date because there is no references to xgbboost which is now dominating many Kaggle contests

While the overview of the content seemed very reasonable both in scope and pacing, the lack of swirl exercises meant that the final project for the course was a bit jostling. Overall, I think this course still needs some development in the way of exercises to familiarize the student with the practical exercises associated with machine learning.

Lectures are too complicated. I understand that material is not easy and one should do a lot of research and reading to understand the essence of the taught algorithms but the lecturer is also not very helpful and assignments are everywhere on the internet which nobody needs to get tired of thinking a little to do the homework as their product.

Like all courses in the specialization, good introduction to statistical learning, although a bit rushed off.

The learner has to navigate through the arcanes of r packages, which is not always easy. I am also quite surprised that neural networks are not part of the course, it should be disclaimed in the course content.

The material is well choosem but poorly explained. This course among all would need swirl excercises, or just more excercises in any form. Instead the lecturer rushes through the material. So in the end you do have some overview about machine learning in R but not enough hands on experie

The quizes do not match a 100% with the lecture videos. There are some weird questions. My algorithms' outputs deviate from answers some times, which is due to different software versions. Quizes are not very educating this time. Courses by Brian Caffo were much better.

Class materials and videos are confusing and do not go into enough detail. Assignments require a lot of search of extra information outside course materials. Also, the length that is needed to complete the assignments vary widely week to week.

Weakest class in the Data Science Specialization so far. Don't expect to leave with a deep understanding of the machine learning techniques covered in this course. You will get practice using the caret package in R, which is very useful.

Although again very interesting, I found the lack of additional materials such as practical exercises, swirls and a book reduced the depth of the course knowledge for me. Maybe we have been spoiled by the previous courses :-)

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.