While the instructor does appear to be very knowledgeable, many mathematic concepts are brought up during this course that are not always followed up with implementation in Python. For instance, there is a demo in Python for Linear Regression and Autoregression, but some brought up methods are not demonstrated in Python. It is a shame, though, because this course had a lot of promise.

Course contents are very good, able to learn a lot.

However, very frustrating system is - project assignment submissions of last week has to wait for infinite time to be graded by peers. Wait time to get feedback on your submission is extremely long and very annoying to have such a long wait.

Either, mentors of this course should step forward and help in this review process at periodic intervals or, this system should go away and it should NOT be mandatory requirement to complete this course.

For poor grading system that is in place for project submission - am submitting 2 stars, otherwise I would have gone for 4 or 5 hands down

This course takes you from learning to do many data analytics and Machine learning tasks manually to all the way doing it much more efficiently using the standard libraries. Overall, a great course to give you a rock solid foundation in this field.

this platform provides an oppertunity to spread my knowledge beyond my careerline

It is very helpful and improve more knowledge

Very Informative 😀

Very good learning

Excellent course.

Helpfull course

LEARNT A LOT

Good course

good course

Nice course

Excelent

Awesome

good

Nice

Good

Good

Good

nice

na

Ok

G

This course provides practical techniques used for regression and classification of datasets. These techniques are important to gain understanding and experience in building a data pipeline in the design process. Logistic Regression, Support Vector Machines, and K-Means approaches are covered along with Jaccard, F-1 error evaluation and Gradient Descent.