The instructor basically reads the slides line by line, with very few examples.

A very good course, it gives me a general idea of how clustering algorithm work.

Redundant, poor explanations and a complete lack of examples about the general concepts and the foundations of this discipline. The interaction between the teacher and the slides is limited to a reading exercise that does not provide any add value at all. Very dissapointed and still wondering if this course is worth my attention -and extremely limited time- or not. Plenty of room for improvement.

I have sat through 4 of the lessons and I am not very impressed. I fell that the topic is very interesting, but the professor does not do a very good job explaining the algorithms. It may be because I do no have the textbook, but overall a rather poor course. There need to be a little more explanation beyond the slides.

it was a really good experience. this course has given me good exposure to data mining

I feel like the programming assignments could've been more involved/tied to the clustering algorithms themselves, rather than just submitting a text file with results (e.g., maybe solve a practical problem with an algorithm of choice). Quizzes sometimes contained ambiguous and/or poorly-written questions/answers. Some of the later lectures simply featured equations on a powerpoint and did not involve any examples on how to use them.

For some reason this course felt like it was hurriedly put together. At times the lectures were great, but many times a topic would literally be covered for seconds that would somehow become an involved quiz question. Now I don't mind briefly covering topics, understanding that cluster analysis is a complex topic with many facets. However the quizzes should reflect the lectures. Overall the course felt more like speed dating, when it should be more about the fundamentals of dating.

Course is very good I learnt about a lot of things related to clustering. Actually it is a very good introductory course in clustering compared to the resources available online in general. Although few things that I think might help improve the course

i) Course only implements K-Means which is a very simple algorithm, instead of this or in addition to this implementation of few advanced algorithms like DBSCAN or CHAMELEON should be added.

ii) A no. of times prof only seems to be reading the slides which make things a little bit unclear i.e, the sentences used should be more common or explanatory rather than just reading the slides which the student itself can.

Apart from these things I truly enjoyed and learned many new things.

Thank you everyone involved in developing this course

Great course that provides a good overview of different clustering approaches and how to deploy them to various problems. I found the lecture material unclear or vague at times, so that for certain topics understanding heavily depends on one diving through the provided reading material (which I found very helpful). However, the topic of evaluation is very dense in the lectures and the provided book chapters do not provide relevant insights as well, making the programming assignment for this part quite challenging (at least if not already deeply familiar already with the concepts involved). Be ready to invest effort to make the most of this.

Just read the slide., The presentations add very little since the presenter is (stumbling) over just reading the text on the slides.

too theoretical without enough practical quiz and assignment

This course is a great resource to learn about the different clustering algorithms out there. I need to solve a clustering problem in my research and my knowledge about clustering ended at kmeans. The course teaches systematic ways to find out whether you should be clustering your data in the first place, what clustering algorithm should be best for your data, and how to evaluate the goodness of the algorithm and the used parameters. Many unknown unknowns have been illuminated to me by the course.

Feel good after all! For me it is a very detailed data mining course with a simple structure and power ideas among various clustering algorithms. Also it is really applied course which opens simple mechanics to see labeled clouds in data. By the end I feel that combination of this course with any other idea gives a quite interesting startup: just mix hierarchical clustering with your ideas)

This course along with the Reading material proposed will give you a big picture of how clustering algorithms work, as well as clustering validation methodologies. It is really useful if you are thinking about applying such algorithms and understanding the state-of-the-art.

This course is very informative! It provides a skeleton for clustering analysis. It is well-design from basic concepts to advanced clustering techniques. I would definitely recommend this course for both clustering beginners and intermediate learners!

Gave a very good understanding of cluster analysis - explaining all different methods and algorithms, the benefits and drawbacks of each. The tool ClusterEng looks very good and can help in a lot of situations. Thank so much

Awesome course but very demanding (if you really want to write everything down). 16 hours if you only breeze through the videos. Because of this a lot of technique are covered with a lot of additional resources.

This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.

Very informative lectures, wonderful assignments. This course isn't so easy but it gives you real knowledge and useful experience.

Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks