學生對 约翰霍普金斯大学 提供的 实用机器学习 的評價和反饋

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.

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.

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!

More practice Items are needed

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

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.

That's a pretty rushed course. I think you really should reformulate it and discuss its content with a deeper way.

Not enough context for the price payed

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!

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.

I'm a big fan of the John's Hopkins Data Science series on Coursera; however, they definitely "phoned it in" on this particular course. No practical assignments except for the quizzes and final course project. Too much deference to outside materials, i.e. if you really want to learn these concepts take Andrew Ng's class or read The Elements of Statistical Learning. The video lectures just breeze over the concepts and leave too much for the learner to just go and figure out. The quizzes, instead of testing your knowledge are literal the only practical learning in whole class. The course project is what you make of it.

By far the laziest course set up in the track. It's an interesting topic, but without independent study I would have learned almost nothing due to the lack of any "practicals" in this "Practical Machine Learning". A really disappointing course that fails to be worth more than just a couple hours of youtube.

Extremely confusing. Should look at Prof. Andrew Ng's machine learning course for how to clearly convey an idea.

This course was a big let-down compared to other courses in the specialization. It doesn't seem like a lot of effort went into course planning and creation. Much of the content is unclear and there is little depth. course textbook, and some swirl exercises would have helped.

very good practical experience using machine learning models, especially regarding PCA usage

Introductory course but it explains the basics easily

nice pace, good overview to start with modelling in R

Excellent.

thank you

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

Wonderful course and instructor, it was the best in the specialization courses so far.

One note is that for most of the methods the explanation was too much precise and short and needed to reinforce it by extra material

This is the most interesting of all the courses in this specialization. Sometimes the content covered can be overwhelming. But the end result in the form of project assignment is worth all the efforts.

This was one of my favorite courses in the specialization as it was so easy to understand and follow. I think the basis I was given has really made me want to delve deeper into the topic and apply it to my career. Thank you!!!

great course in R, really covers the fundamentals.