Aug 25, 2016
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.
Jan 17, 2017
Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.
von kripa s•
Apr 30, 2019
One of the best training experience...
von Dennis S•
May 19, 2019
Amazing course. The Instructors did an awesome job of preparing and presenting the material.
I think there is no better and more approachable in-depth course out there. Thank you so much!
von YASHKUMAR R T•
May 31, 2019
Awesome course to understand the concept behind Gaussian Mixture model.
von Dohyoung C•
Jun 04, 2019
LDA is little bit difficult to understand, but K-mean and Mixture models are easy to understand and quite important for clustering..
von Aakash S•
Jun 19, 2019
Such a clear explanation of topics of clustering. Without doubt one of the best in business.
von Mohamed A H•
Jun 20, 2019
A very rich of useful materials course. The instructor has a fantastic explanation ability. The course is pretty organized and the assignments solidifies the understanding of the concepts well.
It was an amazing experience!
von Jafed E•
Jul 06, 2019
I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand
von Yufeng X•
Jul 09, 2019
It opened the door to more advanced techniques.
von Banka C G•
Aug 10, 2019
Its my great experience for step by step modules
von Shuyi C•
Aug 19, 2019
I think it is easy to understand and good to practice. Nice entry level course!
von Big O•
Dec 21, 2018
More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!
von Srinivas C•
Jan 07, 2019
This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.
von Sundar J D•
Sep 26, 2016
Great course and awesome teaching by Prof. Emily Fox. Prof. Fox did a great job of teaching some of the really tough components (GMM, LDA, etc) in simple and lucid style (like always) and that made it easy to understand and comprehend those topics.
The one thing that I felt had gone down compared to the previous 3 courses was that for some of the topics, the material felt too short and felt like it was cut down to fit within the 6 weeks course duration. I would have at least liked some extra reading material or references especially for GMMs, LDA, Gibbs Sampling, etc.
von Adwait B•
Jan 26, 2018
Great Course! Tough topics well taught
von Steve S•
Aug 26, 2016
Like all the courses in this specialization so far, the material has been good. The reason for only 4 stars rather than 5 is the difficulty in getting questions answered in a timely manner. There don't seem to be any active mentors for this class.
von Sander v d O•
Oct 18, 2016
All the courses in this specialization are great, but compared to the other 3 i did until now, this one seemed a bit short on material. Especially week 1, and somewhat week 6 was without good material. Weeks 2, 3 and 4 were great. I got lost somewhere in week 5 on collapsed Gibbs sampling.
Still: very much recommend this course, it provides a good introduction to Nearest Neighors, K-Means, Gaussian Mixtures and LDA. Thx prof. Fox!!
von Pier L L•
Aug 02, 2016
Very good course nice practical approach. I was kind of surprised that hierarchical clustering was kept at the end and discussed only marginally since it is a widely used approach.
I liked the part about LDA but IMHO I would have liked more discussion about fundamental techniques rather than such an advanced method.
Too focus on text data. Most of the application I worked on have limited textual data.
von Keith D•
Jun 19, 2017
I'm disappointed that courses 5 and 6 of the specialization were cancelled. The cancelled capstone was why I purchased this specialization package.
von Andrey C•
Apr 10, 2017
Overall is great. The LDA and Dendrograms lack quality/specificity and depth of the previous topics. So sad the Specialization collapsed at 4 courses instead of 6.
von 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/
von ELINGUI P U•
Aug 20, 2016
Great course like the others
von Yin X•
Nov 04, 2017
I really like the content of this course, like other courses in this specialization. However, for the assignment in module 5, one must work with GraphLab to get the correct answers in the purpose of getting a certificate. I think it is not very convenient for those who may have trouble accessing graph lab. I wonder if the instructors could provide a pandas/scikit learn version for assignment 2 in module 5. Thanks again for putting together such a great specialization.
von Dony A•
Jan 05, 2017
awesome clustering course
von 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.
von Maria V•
Aug 02, 2016
The specialization has a good quality on average. I started doing this course immediately after it went open. I had a feeling that the quality of the course went down (questions were often unclear and it took time to figure out what is expected as an answer). However, many problems were solved quite fast and teaching stuff is really helpful.
I still would like to see more about MapReduce in-depth in this course. I did not have a feeling that it was covered sufficiently (only theory, no hands-on material). In general, hands-on material was great and useful.