This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
par Dr. P N•
A wonderful learning experience !
par Pavan G•
Explained with nice examples
par Leela P•
Very useful and well taught
par AJETUNMOBI O•
par Christopher D•
par VIDUSHI M•
par KRUPAL J K•
par Oren Z B M•
par Hernan C V•
par vaseem a•
par Alan J R•
par Valerie P•
par geoffrey a•
Good, thorough coverage -- for a 4-week course -- of how to cluster. I liked the evaluation of clustering topic especially. Very few other instructors seem to discuss the vitally important evaluation of clustering results in any depth when they teach clustering. Dr. Han explained a comprehensive framework for understanding the effectiveness of any clustering system. I had never seen some of this material before, even though clustering was a topic appearing in a couple of other data science or machine learning courses that I have taken in the past. Ideally I would even wish to see this course extended to 6 or 8 weeks, so that case studies on difficult real datasets can be clustered. For example I had a terribly difficult ordeal last year before I took this course, trying to cluster the Kaggle.com dataset of the BOSCH competition. It has about 90% missing data in every row, and there are 2 million rows in total, and about 4500 columns! Kaggle's BOSCH is a SUPER tough dataset to work with! I hope to come back to try the BOSCH dataset again using my new knowledge of clustering some time soon. The reason I chose to run unsupervised clustering on this BOSCH dataset, which is ostensibly intended for supervised learning, is to eliminate significant amounts of the missing data from being exposed to multiple individual supervised learning models by prior clever grouping of examples. I am still postulating to the current day that clustering and creating another unique supervised learning model for each cluster is the most important step to eliminating missing data in this particular problem.
par David M L H•
Enjoyed the course. Though there is no programming content, the assignments require such. So, participants should have some prerequisite skills in either R, Phyton or other statistical software to perform. What I like is that the contents cover the "maths" of cluster analysis, though not very deep.
par GANG L•
This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.
par Devender B•
Useful theory. It will be challenging for non-math students. and also lecturer's native language influence iis going to be challening as well to follow along.
par Umesh G•
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
par Alexander S•
Good course. Some of the slides have value errors. Explanations for the programming assignments could be better.
par Anubhav B•
The course is very insightful and very helpful for the data mining studies at university courses.
par Ridowati G•
The material is too general, does not provide examples. So it's difficult when doing the exam.
par PREETAM R•
Covers great deal of topics and various aspects of clustering
Very detailed introduction of Clustering techniques.
par Venuu M•
The course helped me a lot. I loved this course
par Yogesh S M•
Learnt More Here Than I Did At My College!!