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
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 HITESH D•
Installing software parts gave me a very hard time.
By Bastian M P•
Could go a little more in detail on the algorithms.
By Jaime O•
The Deep Learning part needs to be improved
By Chen S•
Very basic, the quizzes aren't clear enough
By Li-Pu C•
A little bit too easy, but good for rookie
By Harsh V K•
Should use Python 3 instead of Python 2
By Phú L L H•
sofware guideline is quiet useless
By Yu G•
No idea what to write here...
By Jorge A C C•
It is a very simple course.
By Ricardo S•
Feels a bit out dated
By RAGHUPATHI R R•
Good for knowledge
By Fredick A S•
By Nasimul J F•
By Kai C•
By Geetha G•
By Anshu R•
By Shahriar K S•
By tarun v s n•
By Abhinav S•
By Bindra B•
By CHEE W M•
By Andrew S•
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 Jean T•
(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 Herbert K•
Even though the course discusses relevant topics, the level is extremely low: The lab sessions were easily solved applying copy-paste code from the provided notebooks, with minor adaptions. Moreover, 8/10 questions in the lab sessions were not related to machine learning at all, but simply looping over data and counting or similar. The intro video and course introduction strongly suggested using deep learning in the course: did not happen. We trained k-means on pre-computed features which happened to come from from a deep learning network (not sure which one, inception? I didn't even watch the lectures here from disappointment). That is not deep learning, it just shows you how well deep learning can work.
Graphlab is a mature framework, I guess, but it's commercial and scikit-learn is better imho (and free!).
If you wish to learn machine learning, take the Stanford course on Machine Learning for Andrew Ng. This course is in MATLAB, not ideal for machine learning, but adequate for a better understanding of intelligent system implementations.
Maybe the course is OK if you're a beginner in machine learning, but not good.