It needs to be updated. Its probably one of the most in demand skills in the field and this has a weeks worth of content 1 section 25 minutes of video 5 questions. Its just not as good as any of the other courses. 100% needs to be revamped.

lecture material could be cleaner with fewer errors

Lectures and course material is insufficient to get the right amount of knowledge to be able to do the tests and the course project

More practice Items are needed

Unsatisfactory and poor course in this specialisation. There are many important parts which are explained inaccurately. In many cases, the lecturer jumps from important points, or assumes students have detailed knowledge about the topic. You can find ambiguity in weekly questions. Very unsatisfied!

I was rather disappointed with this course. I guess it fills the objective of getting you using the caret package and getting you started with some examples. However to understand what you are doing you should defintively go somewhere else. I definitively missed some swirl exercises and more flow diagrams in the slides. It felt for me as I was just copypasting some code from the slides. The course does clearly give some good literature and places to go for details.

The worst course of the specialisation so far. The quizzes are full of typos, not clear at all, and the videos teach nothing, always refering to elements of statistical learning book. Now that I have completed the course, I do know a bunch of algorithm names involved in machine learning, but I certainly do not understand what they do and when using them.

I have passed 5 courses of this specialization and I am not fully satisfied with this one. The course is a very brief introduction to practical machine learning, as the concepts are explained very fast and without a minimum level of detail. Then, most importantly, there are no swirl exercises, so it is quite difficult to put the acquired knowledge into practice. The other 4 courses I took, they all had swirl and that was great. Nevertheless, the course project is quite nice in order to face a real machine learning problem.

Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you

A fast-paced course that got me going in building models and understanding the pitfalls. I felt the directions for the final project were somewhat poorly worded and vague (and calling one of the files test when it was not to be used for testing the model was initially confusing), but overall it was good. I would have liked to have seen the final project uploaded as a secure file as has been done in other courses, and Github was a poor platform for viewing html files. Additionally, the question about out of sample error caused many people problems in the projects as they confused it with with Accuracy, yet it was weighted heavily in the rubric: I'd like the instructors to review the materials how that material is presented in terms of models.

As always with this specialization, you are really just given a taste and there is no way you can fully explore all the material and references presented., but it is enough to get you going and wanting to come back and explore the material more.

So far, one of the most fulfilling courses in the Data Science specialization!

Worst lectures! Worst teaching! I leanrt most of the topics on statquest. Very very very highlevel teaching, very little effort put in by Bcaffo and Rdpeng on this! So many issues in the quizzes. Wasted hours on puzzling out what is to be done! Have a look at the complaints in the course era discussion board. Issues since 3 years are not corrected. The course needs an update. But no m*****F**** is listening. Solutions to quizze are wrong! I have had it with coursera and their useles peer correction. You don't even know if what you are doing is right! Worst FEEDBACK ever!

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.

elementary course and not too much work

ok

It's good that they teach you basics of machine learing in R (caret package), but it's very introductory course. I definetly recommend this course to beginner, but I also recommend taking more courses on this topic (Andrew Ng's for example).

The lectures and quizzes are based on old versions of R and R packages. This course needs a serious update, as some packages work differently, test answers have changed (but not been updated) and coding along with the videos results in different results. Going to the forum you can see that this has been an issue for a few years now.

Good introduction to machine learning. Provides pretty comprehensive coverage of major algorithms and approaches.

Really enjoyed this class and learned a lot!

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

Great course and farily challenging exercises! Thank You for putting this together!!

Include more swirl practice problems.

A key course everything has been building towards, some important concepts and modeling techniques are introduced. However Jeff rushes through a lot of material, and I think this would be better served as two courses with more case studies and exercises, especially as the capstone doesn't use much of this. But nevertheless a useful introduction to this topic, concepts of training vs. testing etc, different models to be used, along with the caret package in R.

Instructor was clear in his explanation. Would prefer to have more hands on exercise for practice

a quick introduction to the basic algorithms for machine learning in R