Well, I am a big fan of Andrew Ng, his initial ML course is what kickstarted my career change from a computer scientist to an established data scientist, I quite liked the Deep Learning Specalization, but this course is absolutely not what I hoped it would be. Explaining what a confusion matrix is in an MLOps course? Explaining precision, recall and F1 score? Come on. That is not content I want to hear about when paying for an MLOps course. Data augmentation and feature engineering? Also, not MLOps topics. A lot of important topics are briefly discussed, but not in detail. Quite a bit of content is rehashed from the Deep Learning Specalization. Good content, but this course is not the right place for that.

I was quite disappointed, as it feel that this 1 week course could have been summarised in a 15min article: there are a lot of generalities, repetitions... and no hands-on assignments where you are effectively expected to code something (which is, to my point, fundamental to get something of ML mooc).

We need more practical graded exercise lab to hone our technical skills, the labs in most cases are easy and not job specific

too much theory, the course could include some lab practices and be more fun and memorable