No math , only superficial concepts. Not recommending to anyone else..

Many typos and incorrect quizzes that haven't been fixed after several years.

This course is great from a coding and final project point of view. in this course I learned how to explore the different techniques and algorithms available to cluster unlabeled data. the notebook and videos are very great too. they walk you through the coding prospective step by step. but from the theory point of view, it is hard to well understand it well in these videos. you have to be aware of them first or study them on your own. although the quizzes aren't that much indicative about understanding. they need to be tougher and contain more questions. the last thing we should be provided the lecture sildes.

As usual with IBM courses, the concepts are well explained and the split between theory and demo on python is very useful. However in this specific course there are a LOT of mistakes in graded tests, which have been spotted by users for months but are unanswered by course owners in discussion forums. It is a shame, and hopefully the last two modules of the professional certification are benefitting from a better maintenance.

It is a beautifully crafted course that looks at various clustering algorithms. More importantly, show the pros and cons of each algorithm/technique based on different patterns.

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

Very Well Structured, concepts clearly explained, lots of Labs to get a hands-on practice and in the end a summary of all the key points explained.

A couple of Labs for DBSCAN and Mean-Shift would have been great.

The concept of SVD with the matrices was not very clear from the videos. Maybe some detailed notes on how the matrices are divided into the submatrices could be really helpful.

This was a very useful overview of two types of unsupervised learning - clustering and decomposition. I had a passing familiarity with some of these techniques, but this course introduced me to a wider array of techniques I had not heard of, along with the underlying theory and comparison between models.

This course enabled me to further develop my standard work process in performing Machine Learning activities. It also expanded my existing skills set with the addition of Unsupervised Machine Learning methods --this actually significantly improved my model performances.

I found the learning experience extremely good and absorbing. The approach of the program to impart theoritical background of algorithms before taking of Labs is very helpful. Also, after the course one gets a broad view of the contexts behind different approaches.

Excellent course on unsupervised ML. Clustering, dimensionality reduction and even classification are very well explained and practiced with high level coding on Python. Thanks IBM.

Great course. Maybe there is one instance of wrong answer in one of the quizzes. Everything elese is perfect. Thanks IBM !

Great mix of theory and application, not too superficial and not too deep. Amazing experience!

Excellent course for me! I had a lot of "Ah ha!" moments during the course! Phenomenal!

Exceptional content. Thank you so much for taking time to create this for us.

Sometimes so fast, but it motives to research more and more about ML.

Excellent course! Just examples of clustering could be a bit better.

Thank you Coursera.

Thank you IBM.

Thank you to all instructors.

Excellent!! Easy and good way to learn unsupervised algorithms!

A high quality course with lots of practical techniques

Great course for learning about Unsupervised Learning

Awesome and wholesome explaination of the concepts

Again, congrats to the instructor on the videos.

Well structured course with many examples

i wouuld have liked a notebook on dbscan