Very good

Awesome!!

good one

Thanks

got it

goods

G​ood

great

Good

Nice

The course touches all the topics that are of interest for the a Machine Learning pratictioneer. I've found the course sometimes oversimplified, that paradoxically made it harder to grasp some concepts, expecially the topics of the Week 2. Overall I've found It to be a good course because at least it gives you the path to follow from where you can study on your own to go deeper in the topics you are interested.

Note: I would suggest to edit the notebooks. It is not a good idea to have the solutions in the same notebook where you should do an exericise, because it makes also the video lectures that came after pretty useless. I suggest to separate the exercises from the solutions, and to put the solutions in the video lectures so you must follow them with some focus to understand what the solution was. Furhtermore i would review the notebooks. Some of them were different from the ones presented in the video lectures which made it a little bit confusing to follow.

The quality of this course is very good. It helped me to get a basic understanding of exploratory data analysis. Whereas the first weeks topic was more or less early for me, the seconds weeks topic about statistics was more challenging and I also had to do some own research to deepen the contents discussed in the lectures.

I really liked this course, has been extremely useful for me as a starting point for next IBM courses. One suggestion to improve - some concepts are covered a bit superficially, in my view, e.g. Hypothesis Testing. Maybe going a bit more into theory would help.

The information was good though basic. I thought the info on hypothesis testing and probability was probably not necessary for an ML course where this should be assumed. The teacher was clearly reading off a script which was at times not so engaging.

The course is great but in my opinion the teaching material will be even better and more exciting if it has less text and more graphical visualization of the topic being explained. I feel like the instructor read the slides for me.

It is fairly difficult for a beginner - although the level is intermediate for this course and there are a few prerequisites, somehow I still feel that a lot is pending to be explained, esp. in the DEMO/LAB exercises

Excellent content and examples. Would be great if another example for hypothesis testing is added to demonstrate this concept with a typical ML dataset (maybe use one of the previous datasets used during the course)

Great instructions, wonderful demos and insightful comments on the results. The only part that I did not find well explained is the part on hypothesis testing. Some details could be added on t-test and z-test.

It lacks a deeper view of the topics and applications on programming for a real world Exploratory Data Analysis (EDA), but gives the basic tools and understanding to introduce you to EDA.

The first week of this course was very informative and with a lot of examples. Although, the second week was difficult to understand, the concepts nad the examples were not clear.

Good introduction. The time estimates to complete assignments are off.

Need a lot more material and direction for assignments to aid learning.

I know some basic statistics knowledge is required, but sometimes the analysis story is unconnected, and sometimes make the story confusing.

The course is good. What it lacks to get the 5 stars is to be able to download the slides of the classes or to have a documentation.

It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.

In week 2, the lessons were a bit in rush and it would be better to have a bit more detailed discussion.