I liked the theory parts, but had a to of problems with the hands on exercises: I spent a tremendous amount of time on installing/trying to install the necessary software. And not everything worked properly on my Mac Laptop.

Very basic things... Any examples for regression.

A bird's-eye-overview introduction of the field. It teaches you some terms and it gives you ideas about which fields might be interesting for you if you want to really learn how to do machine learning with big data.

It's ok for an extremely high-level overiew

It was a basic course for initial understanding about Machine learning.

Nice knowledge refresher

I miss some technical information about machine learning techniques such as neural networks.

The course should cover more topics about Machine Learning.

The Hands-On exercises were good. The theory part was too shallow.

This course is more "the very basics of machine learning" illustrated with some examples. The lectures were clear and logical, but honestly, very basic. Unfortunately the big data handsons (the ones with pyspark) are not explained very thoroughly, often they just state that "do this or do that" instead of explaining what is going on. All in all, I have expected more big-data related topics and less introduction to machine learning.

The parts including KNIME was not interesting or educational, it was just an big grind. I feel once you are on a level to use KNIME you know that it is better (and easier) to use other frameworks where you have more control, therefor missing customers the program is meant for.

Additionally the last hands-on felt rushed and just copy-paste to some extent (to being able to complete the tasks), even for me having a lot of jupyter and machine learning background.

Almost a useless course - ML overview using KNIME which gives no insight whatsoever.

The virtual machine in this course no longer is functioning. PySpark update seems to not play nice. I think the content also needs some updating for more modern machine learning techniques.. like using big data with deep learning systems like tensor flow or PyTorch.

Machine learning is too simplified and spark part is not even explained, also very little relation of all course with Big Data.

Too basic

I expected course with more in-depth and more difficult examples, I learned about a few new concepts, most methods were repetitions for me.

This is another course in UCSD's "Big Data" introductory course. The material is not pertinent to a specialty on big data technologies. Further the course does not increase one's knowledge of Machine Learning in any way that justifies spending the time in the course.

The course content is very poorly explained. The quiz questions don't really test what was taught in the lectures, and the assignments are just copying and pasting things. I feel like I still have a very poor understanding of what was supposedly covered in the course. I cannot generalise or apply the 'learned' information or skills to other topics or researches because I didn't actually understand the core concepts or how to use the programs.

Regression is least bothered, less hands on