Jan 16, 2017
excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!
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
By 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
By Lucas F M•
Jan 11, 2022
There is nice information, but it was thrown around. It lacked pedagogy. They did not pay much attention to updating the quizzes to make sure students would be able to find the correct answers easily. A good course, but much to improve.
By 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
By 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.
By 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.
By 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.
By Anju M•
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
By 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.
By Timothy 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.
By 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?
By 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.
By Michael R•
Oct 3, 2019
It's a mediocre intro to some machine learning tools. I think the course materials could be drastically improved.
By Philip W•
Jan 30, 2019
Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.
By Victor M C T•
Sep 13, 2022
This course does not give a clear understanding of the concepts for Machine Learning.
By Allister G A•
Dec 25, 2017
The course needs to elaborate more on hands on discussions.
Jan 18, 2017
not what I expected for a machine learning course
By Yohann B•
Feb 6, 2016
incomplete and not clear. extremely disappointed.
By Yang L•
Aug 14, 2016
needs more case studies and examples
By Haolei F•
Mar 13, 2016
Need to get more in-depth
By Naman D D•
Aug 31, 2020
Very vague as a mooc.
By Jason A C•
Mar 8, 2022
The instruction was of high quality as usual.
However, the course has not been kept up-to-date with the software needed to complete it. I tried first to use RStudio on Mac OS "Big Sur", but some of the packages needed to complete the quizzes and assignments would not run on that version of RStudio. I tried using RStudio on an older machine (Mac OS "High Sierra"), and managed to load packages that would generally work, but they would sometimes give results that were not expected by Coursera. I did my best to figure out what the answers should be, but did not achieve the perfect scores I usually try to earn.
I spent a great deal more time on this course than it should have taken. I learned some good material, but at the cost of excessive frustration and wasted time.
Please update the assignments to match the latest operating systems, software, and packages! Please maintain properly your online courses!
By 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.
By 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.
By 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.