Learner Reviews & Feedback for Practical Machine Learning by Johns Hopkins University

Course content feels upside down. You'll learn about machine algorithm specifics and caveats before anyone explains what the said algorithm actually hopes to achieve.

This is a part of the data specialization; from afar, I would not be interested in Machine Learning because of this course. I will seek other methods to learn.

It is a poor course… A lot of the materials go to Wikipedia or other sites. What is the point of a course that sends you to Wikipedia?

Course project was the only project work, needed more. This course should also use swirl(). Quizzes et al contained mistakes.

It's a mediocre intro to some machine learning tools. I think the course materials could be drastically improved.

Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.

The course needs to elaborate more on hands on discussions.

not what I expected for a machine learning course

incomplete and not clear. extremely disappointed.

needs more case studies and examples

Need to get more in-depth

Very vague as a mooc.

Gosh I hated hated hated this course. Nothing to learn here. You will just be given lots of names with no explanation whatsoever.

I often felt really angry at the teacher because of the way he would introduce entire prediction models without explaining anything about them. Also, I really didn't like the fact that the course is centered on caret, a "shortcut" package to do stuff fast. Before doing things fast I need to know what I am doing! Finally, the quizzes and assignments are completely disconnected from the courses.

The worst course I have ever taken on coursera.

This course did not fulfill my expectations. It is the worst one in the Data Science Specialization by far.

Although the explanations are fine, sometimes they are too vague and there is no practice at all, when the title of the course is "Practical".

Most of the tools used are not comprehensively detailed and the quizzes are quite confusing.

Some of my peers reported that the course is not updated since 2013, which is a severe flaw when talking about one of the statistical tools more in-fashion nowadays.

The professors are experts on the subject, but unfortunately they rush through content and some of the classes are outdated (i.e. they use packages and data that are not the newest version) and this generates confusion througout the course.

Material is very high level. No ppt's are given, so all links presented in the video's cannot be viewed.

Quizzes are based upon old packages, so incorrect answers are provided.

No replies at discussion board from TA"s or instructors.

Poor compared with some of the others on this specialisation. The lectures are too fast and high level, with no allowance given for people who are unfamiliar with this area and attempting to learn it.

Very little depth. I don't recommend this if you don't already have background in statistics or R. I really didn't learn anything. I mostly just gamed the quizzes and projects.

Cannot take the exam, I have to pay... wtf... I will probably pay at the end, but I want to take the class first. Without certificate I cannot prove I took the course.

They explain nothing on the fundamentals of the machine-learning methods, nor how to know which method apply to a given problem.

This course is NOT for the beginner. Take time to finish all the beginner and foundation courses and then take time to learn R

I wanna know, what is happening with my grade with this course. I still wait long time ago. Please, help me.

In my opiion, this course needs to be improved a lot. There are almost nothing Practical Machine Learning.

too general no depth, not recommended for science or engineering degree holders

To be honest I don't think this is worth the money.