学生对 约翰霍普金斯大学 提供的 实用机器学习 的评价和反馈

No es un curso en el que se aprenda demasiado.

Parece demasiado avanzado en el uso de "caret" y en vez de enseñar, parece ser que todo debe ser aprendido con anterioridad.

Todo el material adicional que se necesita en el curso, es en general contenido externo.

I saw two main issues with this course: 1) dated lecture videos, oftentimes with R code that can't be replicated using up-to-date packages, and 2) lack of thoughtful design: example after example after example after example doesn't really teach you anything.

This course is rather bad, not well rehearsed and hastily delivered. Especially in comparison with other, in-depth course of this Specialization. The course is more of a 'caret' package review then actual Machine Learning. I learned how to use the

lecture can be really unclear sometimes because lecturer breezes through the actual implementation of training/predicting: "use x, y, and z [underlines some stuff on screen]" and you're done

Also lots of mistakes/typos in lecture and quizzes

This is too high level for a machine learning course. You don't exactly learn a lot about the techniques just how to use them and name them out if you're having a conversation with a person. My least favorite course in the series

This course is fairly old and it's starting to show. Quizes require you to install versions of libraries that are multiple releases back and I ended up spending more time doing that than I did building and understanding models.

Very basic, might as well just read a cheat sheet. No explanation of how or why to choose different options in a pipeline, for example, which data slicing to use (k-folds, bootstrap, etc). Just runs through how to do them.

Very shallow content - broad, but not deep. Not many assignments instead of the last one. We hear what we heard before. For the same price, Analytics Edge at EdX is far better choice for practical machine learning.

Felt difficult in understanding the overall course in short duration . 1 month is not enough for this course. I request the authors to make the course much more simpler

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.