SO
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A great course, well organized and delivered with detailed info and examples. The quiz and the programming assignments are good and help in applying the course attended.
BK
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excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.
By RISHABH T
•Nov 12, 2017
excellent
By DHRUV S
•Nov 4, 2023
good one
By Iñigo C S
•Aug 8, 2016
Amazing.
By Mr. J
•May 22, 2020
Superb.
By Zihan W
•Aug 21, 2020
great~
By Bingyan C
•Dec 26, 2016
great.
By Cuiqing L
•Nov 5, 2016
great!
By Job W
•Jul 23, 2016
Great!
By Vyshnavi G
•Jan 23, 2022
super
By SUJAY P
•Aug 21, 2020
great
By Krish G
•Sep 7, 2024
NICE
By Badisa N
•Jan 27, 2022
good
By Vaibhav K
•Sep 29, 2020
good
By Pritam B
•Aug 13, 2020
well
By Frank
•Nov 23, 2016
非常棒!
By Pavithra M
•May 24, 2020
nil
By Alexander L
•Oct 23, 2016
ok
By Nagendra K M R
•Nov 10, 2018
G
By Suneel M
•May 8, 2018
E
By Lalithmohan S
•Mar 26, 2018
V
By Ruchi S
•Jan 23, 2018
E
By Kevin C N
•Mar 26, 2017
E
By Asifur R M
•Mar 19, 2017
For me, this was the toughest of the first four courses in this specialization (now that the last two are cancelled, these are the only four courses in the specialization). I'm satisfied with what I gained in the process of completing these four courses. While I've forgotten most of the details, especially those in the earlier courses, I now have a clearer picture of the lay of the land and am reasonably confident that I can use some of these concepts in my work. I also recognize that learning of this kind is a life-long process. My plan next is to go through [https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370], which, based on my reading of the first chapter, promises to be an excellent way to review and clarify the concepts taught in these courses.
What I liked most about the courses in this specializations are: good use of visualization to explain challenging concepts and use of programming exercises to connect abstract discussions with real-world data. What I'd have liked to have more of is exercises that serve as building blocks -- these are short and simple exercises (can be programming or otherwise) that progressively build one's understanding of a concept before tackling real-world data problems. edX does a good job in this respect.
My greatest difficulty was in keeping the matrix notations straight. I don't have any linear algebra background beyond some matrix mathematics at the high school level. That hasn't been much of a problem in the earlier three courses, but in this one I really started to feel the need to gain some fluency in linear algebra. [There's an excellent course on the subject at edX: https://courses.edx.org/courses/course-v1%3AUTAustinX%2BUT.5.05x%2B1T2017/ and I'm currently working through it.]
Regardless of what various machine learning course mention as prerequisites, I think students would benefit from first developing a strong foundation in programming (in this case Python), calculus, probability, and linear algebra. That doesn't mean one needs to know these subjects at an advanced level (of course, the more the better), but rather that the foundational concepts are absolutely clear. I'm hoping this course at Coursera would be helpful in this regard: https://www.coursera.org/learn/datasciencemathskills/
By Kostyantyn B
•Nov 7, 2017
A high quality, intermediate difficulty level course. The instructors are obviously very knowledgeable in this field and strive to pass their knowledge and skills onto the students. One of the major advantages in my opinion, is the fact that the authors decided to include a number of advanced topics, which you normally don't find in an introductory level course on the Unsupervised Learning. The exercises seem to revolve mainly around the Natural Language Processing, which is fine by me, for two reasons. First, it is a very challenging part of the Machine Learning. Second, NLP is in high demand in the industry. So, I see no downsides here. Plus, there is only so much one can squeeze in a 6-week course...
I would however like to mention that I wasn't entirely happy with the way the Latent Dirichlet Allocation and the Gibbs Sampling were explained. This was the first time I heard about these techniques and I found them fascinating. I understand that these are challenging topics that require a more advanced math for a serious discussion. But I still think it would be worth including perhaps an optional video and/or exercise to go deeper into this subject. I am sure some students would appreciate it; I know I would...
In summary, it is a great course to take. It will help you better understand the theoretical foundations and boost your practical skills in the Unsupervised Learning.
By MARIANA L J
•Aug 12, 2016
The things I liked:
-The professor seems very knowledgeable about all the subjects and she also can convey them in a very understandable way (kudos to her since talking to a camera is not easy)
-The course was well organized and the deadlines were adjusted when a technical difficulty was found by several students
-All the assignments are easy to follow and very detailed
-The testing code provided for the programming assignments is a huge help to make sure we are solving it the right way
What can be improved:
-Some of the concepts during weeks 4 and 5 seemed a bit rushed. Although the professor explained that some details were outside of the scope of this course, I felt that I needed a more thorough explanation in order to understand better
-Some links to the documentation of libraries used in the programming assignments were lacking information on how to really use them, I wish we had some other link to worked examples too
In general I can say this was another good course for this series. Making a course like this is not easy at all and I can see that they are putting a lot of effort to produce them. All of their hard work is really appreciated on my end.