It would be great if we go in a little more details of when to use which metrics for evaluation. Instead of running through a bunch of concepts you could have spent a little more time in each of them.

There were serious problems with this course, not in the instructional material but in the execution. There were multiple typos in the code. The especially grievous ones being in the dictionary names.

This is the 6th course of the specialization and I gave 5 stars to the previous courses. But this course have many typos in videos and codes. It makes harder to understand some points.

The work in this course was incredibly interesting. However, there are many errors and the forums went for over a week without response to questions...It seems hastily put together.

Videos are very fast and dont go into details. Assignment is very easy, it could have been more challenging which can test and make learner to think using several concepts learned.

Typos are very unprofessional and spoil impressions of the course. Tests and labs are super-easy and do not make you think, and you only need to repeat commands from the lectures.

So many errors in materials. It's unacceptable for course of such level. Even though people mentioned these errors in discussion forumns noone seems to bother about correct em.

Some of the codes shown in the videos had minor errors. Also, a bit more explanation for function (in statistics terms) would have helped in having a better understanding.

This is a good course for beginners with Python. The content is explained in a very direct and comprehensible way, but more programming exercises and tasks are required.

There are lots of typos in the labs and assignments, which can be frustrating. I expect better quality from IBM. Content is great and easy to understand nevertheless.

This course is quite good. The bad thing is that the arguments of the last "lesson week" are treated very superficially, taking for granted some advanced knowledge.

A lot (too much maybe) is covered in this coarse

It really helps a lot when you know some statistics. Like linear regression,

Why gridsearch was covered I wonder.

While a bit fast and loose with the concepts, does contain a lot of practical code as to how exactly to bring things discussed about, which is appreciated.

Thanks for teaching me the systematic way to do data analysis! However, I found quite a few mistakes in the lectures in this course, hope it will improve!

IBM Cloud is difficult to use.

The generated link of notebook will not share the latest version, if you click the share icon before editing the notebook.

Data visualization was taught in details in course 7 and regression was taught in course 8. With no backgrounds, the codes in this course are scaring pe

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