Great class!

Very Good

too good

Good.

Great

Good

nice

The difficulty level of the course is as it states, intermediate.

Both the instructors are quite good at explaining things and also provide a little insight as to why they're choosing to do something at any given moment.

There is this one lecture though from a guest faculty that just plain reads out what's written in the presentation slides.

Although they try to explain every short thing, it might go over one's head or require repetition if the reader is 2 or 3 viewings with the mathematical concepts behind the algorithms previously. But the course still felt pretty self-contained to me,

Still, it's an overall balanced course that can't be completed unless one understands what the code is doing. Great for getting insights on and developing data science intuition.

The information in the videos is excellent. I am actually very please by how succinct and clear the topics had been covered. My reason for giving 4 stars is because the programming assignments do not really help crystallize the new material. They may include a fraction of the concepts that are covered. It would be nice if the assignments involve stuff like the inclusion of param grids, comparing different ML algorithms, implementing PCA, etc. Also would be nice if there had been a review of the Fourier Transform material using SparkML.

Overall a decent course. The lecturers could go into more depth with some of the topics they covered to allow the learners to really grasp the concepts. I felt all of the assignments were too simple, possibly allowing you to pass even if you don't completely understand the material. More depth in the lectures and challenging assignments would leave me completely satisfied.

I like the course but I feel that it really needs more depth. It feels like most topics have been just skimmed through and not explained very well. The IBM tag is something that attracts you but if you wanna delve into the details, this course will tell you what to learn, and then you'll have to go on youtube and look for resources.

I liked the course.

I like Week 4 of Advanced ML course. It is very fulfilling.

But, I think the portion of large data handling using parquet and spark is still missing in both the course (Scalable DS and Advance ML). There should be a session where is taught how to create parquet files and how to store them in object storage.

O Curso é ótimo e apresenta muitos conceitos de Machine Learning e Processamento de sinais, mas faço uma ressalva, pois como o próprio nome diz é Avançado e o candidato precisa ter uma boa base de programação, particularmente precisei pesquisar bastante sobre Apache Spark e Systemml pois minha formação não é de TI.

Of course, the course requires that you are somewhat familiar with mathematical aspects of higher analysis!

But...

It shows you the application of this knowledge to real world cases (especially week 4). I found the programming tasks and cases ideal for viewing the different methods.

The course was very intuitive. I have given 1 star less due to the 4th week's assignment. I feel it would have been more useful if there was an assignment where we could use SparkML instead of SystemML as it is not taught well enough in this course.

Assignments were a bit too easy. I didn't really have to understand 90% of the lectures to complete the assignment. Most changes were related to spark.sql knowledge and how to instantiate classifiers and such.

Overall good course; very interesting concepts given in the lectures. I only wish the programming assignments were a little more interactive and deeper than "fill in the blank." Great stuff though thank you!

Very interesting concepts and more math than other courses, which was nice. The audio quality of guest lecturers needs to be improved, but I appreciated the video content and hands-on examples.

Amazing course with real life usecase. A bit more explaination would have helped as most of the content is based on the fact that the viewers are familiar with SparkML/ SystemML

Really good if you know a bit about Machine learning, it's not important to know this in DML, could focus on this in python with scikit but still thoery is very useful

Definitely worth the time, with good practical examples and a ton of maths behind Fourier Transform analysis and applying machine learning pipelines to Apache Spark.

romeo class was too good , his information are too short and catchy , i like this course due to him , i am eager for any other course which he would teach

fairly good. not perfectly organized but a little bit relax. good pace for workshop style training for some parts and enough details for some other parts.

This was a good course but I don't know how signal processing will be useful for some people who aren't in the field of physics (signal processing)

This course help me learn many new concepts but I would suggest some coding exercises in the course would much better for the better learning.