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
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.
par Michael R•
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
par Norman B•
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
par Adam C S•
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.
par Alexander R•
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.
par Stefan K•
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.
par Anju K•
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
par Vincenc P•
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.
par Tim A•
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.
par Andrés M•
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?
par Jeffrey G•
Course project was the only project work, needed more. This course should also use swirl(). Quizzes et al contained mistakes.
par Michael R•
It's a mediocre intro to some machine learning tools. I think the course materials could be drastically improved.
par Philip E W J•
Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.
par Allister G A•
The course needs to elaborate more on hands on discussions.
not what I expected for a machine learning course
par Y. B•
incomplete and not clear. extremely disappointed.
par Yang L•
needs more case studies and examples
par Haolei F•
Need to get more in-depth
par Naman D D•
Very vague as a mooc.
par Gianluca M•
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.
par José M M A•
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.
par Ricardo G C•
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.
par Danielle S•
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.
par Jo S•
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.
par Robert O•
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.