The section on model development and evaluation is not so clear. It is difficult to understand if you have no prior knowledge of machine learning.

Course was great however i felt a lot of material was covered in a short period of time, this course can be 2 or 3 courses based on the content

It is missing a last project like in the two other courses... It would have been quite fun to be able to apply what we learnt in a project.

Lots of spelling and grammatical errors that made it difficult to understand some of the material. It is otherwise an interesting course.

There is no slides to download or review after class which makes it hard to go over the knowledge again without watching the video again

The video is too short and many concepts remain unclear or poorly explained. More contents need to be added to the questions and tests.

It's an excellent course; the best part is the labs. It's a pity that we (the students) have a lot of problems loading the labs.

Little bit confusing, unstructured and not easy to follow. Material inside is good though, but the course needs to be improved.

Things are too fast in this course and many things remain unexplained which makes it difficult for one to understand properly

Too many errors in the code and explanations. Makes it very difficult to understand which is the right procedure/conclusion.

This course focuses a lot on the theory and explanation. However, there isn't much hands-on practice for the coding itself.

*No response to some questions/comments on the forum

*More details/thorough clarification required for some points covered

There are many errors and this was even the toughest course I have taken yet since many things were not explained clearly

Course is flawless but when i had a technical challenge the Coursera team were clueless on how to fix it.

Otherwise good.

Few of the video requires improvement in terms of its quality. Particularly the lectures corresponding to week 4 and 5

Too complex for easy understand. Should have some documentation explaining the process and comparing the new methods.

I​t is okay in the sense that you learn something but the questions should be harder and it should go more in depth.

Topics covered are important but videos end abruptly as if narrator was saying something more and video just ended

why is sharing of the notebook worth 3 points? That has absolutely nothing to do with python or data analysis!

Model creation and analysis part are too short, should have more details to understand the concepts better.

This course seems to have an exponential increase in a learning curve. It seemed to be all over the place.

More explanations would be great. Its very difficult to understand Data exploration / evaluation sections

Creo que le hace falta unas guías, toda la información se da por videos. Recomendado para principiantes.

As always, the final project always ruins good courses. LAZY design of the projects is unacceptable.

Week 4 is somewhat hard to follow - Here, an overview over the different concepts would really help