Oct 17, 2016
Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much
Aug 19, 2019
The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.
By Daniel J•
Jan 07, 2017
excessive use of GraphLab create which is not an industry standard.
By Jean T•
Apr 17, 2017
(1) I feel I spent most of the time learning graphlab. Suggest replace it with standard Python as the standard tool for this class. Provide any needed additional code in standard Python.
(2) Course is better in the front end than in the back end.
(3) Week #6 is significantly more involved than previous weeks. Suggest divide Week 6 into two sessions: Neural Network and Nearest Neighbor applying neural network results (ImageNet 2012 was mentioned and not explained. Therefore the Nearest Neighbor homework assignment from the student's perspective does not have much to do with neural network other than using the results from ImageNet 2012, which was not explained in any detail anyway). This will allow more time to delve into the forward and backward propagation which should have been explained in more details.
(4) Home assignments are not best worded, especially homework assignment for Week 6. Suggest reword in shorter statements that are more to the point.
(5) Programming presentation and assignments can seem like exercise in graphlab and SFrame functions rather than machine learning.
(1) Class presentation by Professor Fox on recommender system is detailed and clear.
(2) Classifier block diagram shown by Professor Guestrin is good, clearly distinguishing training the classifier and the subsequent use of the classification (prediction).
(3) Neural network quiz in Week 6 is excellent. It drills down on the multi-dimensional space that neural network is particularly good for.
By Peter G•
Mar 22, 2016
The teachers are easy to like, but the course content is very lightweight and will mostly teach you terminology with no real understanding.
The worst part was the assignments, which could all be solved by a little copy/paste: I didn't learn anything useful by doing them. All the actual algorithms were supplied in a separate module. More than that, many of the suggested solutions were bad coding (like collapsing 50% of the data before training, or writing sixteen special cases rather than a general function) or pointless (like training a linear classifier on pixel data).
There are better courses out there.
By Najmeh R•
Oct 04, 2016
The subjectes are not learnt deeply and precisely. Too summarized and vague!
By Andrew S•
Dec 03, 2016
The content of this course is interesting, I liked the examples, and the material gave an interesting overview of different aspects of machine learning. From that perspective, the course is as advertised. But, where this course goes wrong is value for money - it is very superficial and not worth what is charged.
As noted by others, this is not a course for learning so much as an advertisement for the instructor's own pay software and their other Coursera courses. I'm not against that per say if it was entirely free, but charging for an advertisement is ridiculous. In my case I thankfully started with the free model so I didn't lose out, but I could see others being dissapointed. I strongly recommend starting the material with a free signup and only pay if you really want the extra grading.
My other main problem was with the pace and detail in the course. I would have liked more detail, but I recognize this was intended to be a high level view so I'll live with that level of detail. The material covered, however, does not need 6 weeks worth of lectures. This course could be ~1/2 as long, cover the same material, and be a MUCH better course.
Other small problems include some poorly edit videos (there are a lot of examples of simple stumbling in the videos that should have meant they do another take), very short videos (maybe a person preference, but the number of <2 minute videos here is annoying, especially when there's a 5-second standard video at the start and end of all videos). All in all, there's just a lot of wasted time.
When signing up for this course I was really excited for the entire specialization - now, not so much. I'll probably try the second course in the series (for free to start) to see if things improve, but ironically this advertisement video has if anything turned me off their other products.
By Arun J•
Sep 18, 2018
not useful since the material covered lacks any rigor.
Jun 24, 2019
Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.
By Annemarie S•
May 24, 2019
The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.
By Matthew F•
Jul 22, 2019
Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.
By Keith P D C•
Oct 28, 2019
Two stars because of GraphLab! Otherwise great concepts!
By Krupesh A•
Feb 15, 2019
Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.
By Kaushik M•
May 01, 2016
Too many videos and not cluttered assignment codes
By Eduardo R R•
Sep 23, 2015
This course rely on commercial library. I am sorry, I don't believe the convenience of a commercial library is good for your learning. You may end up locked in.
By Phillip B•
Sep 25, 2015
Would have greatly preferred if open source tools were used.
By Chandrakant M•
Sep 06, 2016
I felt that I paid for demo of the Dato/Turi.
By Jitendra S•
Apr 29, 2016
Dato tool does not even install properly.. so n´makes no sense to continue with the course. The support team fail to help in installing ... :-(
By Darren R•
Oct 13, 2015
Thoroughly disappointed to see this course based on
By Jonathan W•
May 31, 2019
The course includes some good, basic, information on machine learning. The instructors seem to know the material well. However, the exercises and coding are based on a python package written by one of the authors that, while free to students, does not easily translate into common packages such as Numpy, Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and Pandas. Also, the package used only works in Python 2 (which will no longer be supported as of January 2020).
By Tim B•
Jun 04, 2019
Complete waste of time until it is written using open-source packages.
By Toma K•
Jun 11, 2019
Warning! I paid for the specialization and now it tells me that the course ended 2 months ago!! i can't complete quizes which is why i paid!!! no options available to contact support.... no refund available....
By Pablo S•
Jul 22, 2019
I should have read the negative reviews before wasting two full days trying, and failing, to install the required software. I urge anyone reading this to avoid this course and look for alternatives.
By Natalia Q C•
Jul 25, 2019
The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that! I want MY MONEY BACK!!!
By Nils W•
Sep 19, 2019
The course could be great, if it won´t depend ob Python 2.7 and graphlabs (because scikit isn´t scalable). Also some quiz questions are so hard, that it is impossible to answer only with the material. So they use forum posts to answer how you can find a solution to the quizzez. So in total more a waste of time.
By Youngmin C•
Sep 06, 2019
Too old, bad packages, not much to learn. too basic.
By Nitin K•
Sep 12, 2019
Not good support to learning process.