Jan 17, 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 14, 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
par Mehrshad E•
Mar 28, 2018
This course really lack something like SWIRL. The lectures only provide a summary, which is not helpful for someone new to the machine learning. Also, the instructure tries to cover pretty much everything but not in depth; instead, I think fewer topics should be covered in depth.
par Arcenis R•
Feb 26, 2016
The instructions for the final project were very unclear and even though I submitted all assignments well before their respective deadlines and reviewed the required number of projects my work was not processed for a grade thereby delaying my specialization completion.
par Felipe M S J•
Dec 02, 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.
par 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.
par 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
par 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
par Norman B•
Feb 07, 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
par 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.
par 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.
par 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.
par 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
par 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.
par 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.
par 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?
par 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.
par Michael R•
Oct 03, 2019
It's a mediocre intro to some machine learning tools. I think the course materials could be drastically improved.
par Philip E W J•
Jan 30, 2019
Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.
par 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
par Y. B•
Feb 06, 2016
incomplete and not clear. extremely disappointed.
par Yang L•
Aug 14, 2016
needs more case studies and examples
par Haolei F•
Mar 13, 2016
Need to get more in-depth
par Naman D D•
Aug 31, 2020
Very vague as a mooc.
par 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.