incredible

Excellent!

Fantastic

Wonderful

Excellent

very good

thank you

Excellent

Fantastic

AMAZING!

Perfect!

love it

Great!

great

g​ood

good

nice

Good

nice

​

T​his course help learner to gain key insights from one of the leader in AI field, for developing Machine leanring based applications. Course is keep more on discussion and thoughts than technical (more provided through ungraded lab exercies).

S​uggestion:

I​t would have been better if the graded labs was made part of the grading and had more lab exercies on fastAPI and other topics. (I think, the purpose of the course is to teach it to a larger audience including non-tech people).

I​ enjoyed and learned a lot from the course.

T​hanks.

I was a little disappointed that this was heavily focused on unstructured data, but it was still a wonderful course. Many of the techniques of being "Data Centric" do not carry over as well to structured data. I am hoping I will hear more in the next courses of this specialization that address being data centric with structured data (which would seem to be more applicable to many business analytics cases).

While the overall content of the course for ML LifeCycle is great, the examples and general assumptions are for supervised learning and labeled data, in some real scenarios, having labeled data is just not possible but by no means this indicates there is no possible AI solutions and models that give business value. So a little inclussion of unsupervised and semisupervised learning examples would help.

This course is a practical guide for someone who's interested in developing ML models in real life, make use of it and maintain, improve, and support it for business needs. To those folks whos coming from an academic background and haven't seen the landscape of the use of ML models in real life, this course can be a really good starting point ;).