Awesome

love it

Great!

Great

super

great

good

GOOD

good

Good

good

good

Top!

good

nice

Good

nice

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.

I have worked on a lot of Machine learning projects. It has helped me to organize my thoughts. How to access the best practices beforehand based on the size and type (structured/unstructured) dataset. I prefer more hands-on experience which was missing. I didn't learn exactly something new but it did help me understand how to look at and plan for a new Machine learning project.

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 ;).

I wish for graded programming assignments in this course as well.

Although much of the things talked about here are theoretical, a programming assignment here would be immensely useful to a beginner to get a practical idea about the related concepts being covered and referred here across the couse modules.

This course offers a lot of practical advice, the kind you won't find in most machine learning courses and the kind that you'll use on a day-to-day basis in your career as a data scientist. It's quite easy to follow and appropriate for beginners and non-technical students.