Chevron Left
返回到 实用机器学习

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

3,131 个评分
595 条评论


One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....


Aug 13, 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

Feb 28, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.


551 - 实用机器学习 的 575 个评论(共 585 个)

创建者 Felipe M S J

Dec 2, 2016

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.

创建者 Jonathan O

Apr 18, 2016

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.

创建者 Pawel D

Jan 22, 2017

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

创建者 Michael R

Jan 19, 2016

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

创建者 Norman B

Feb 7, 2016

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

创建者 Adam C S

Jul 22, 2020

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.

创建者 Alexander R

Aug 21, 2017

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.

创建者 Stefan K

Mar 10, 2017

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.

创建者 Anju K

Apr 17, 2016

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

创建者 Vincenc P

Mar 31, 2016

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.

创建者 Tim A

Oct 14, 2016

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.

创建者 Andrés M

Jul 31, 2020

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?

创建者 Jeffrey G

Sep 12, 2017

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

创建者 Michael R

Oct 3, 2019

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

创建者 Philip E W J

Jan 30, 2019

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

创建者 Allister G A

Dec 25, 2017

The course needs to elaborate more on hands on discussions.

创建者 max

Jan 18, 2017

not what I expected for a machine learning course

创建者 Y. B

Feb 6, 2016

incomplete and not clear. extremely disappointed.

创建者 Yang L

Aug 14, 2016

needs more case studies and examples

创建者 Haolei F

Mar 13, 2016

Need to get more in-depth

创建者 Naman D D

Aug 31, 2020

Very vague as a mooc.

创建者 Gianluca M

Oct 20, 2016

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.

创建者 José M M A

May 25, 2020

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.

创建者 Ricardo G C

Jun 17, 2020

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.