Labs take too long to execute

No one answers on the forum.

Very difficult for beginners

beginners, not intermediate

the tool for HW doesnt work

Contains outdated material

can be done much better

Lab are working worst

Good for beginners

Too superficial

Special Course

not bad,useful

Too simplistic

Nice course,

Good Course.

good.

GOOD.

GOOD

good

Good

It's too bad some people with phds and very poor teaching skills think they can write up some code and feel they are teaching these classes. That being said, it's super cheap and it's very easy to find information online to supplement the lack of adequate descriptions of the topics. Changes that would make me more likely to take another coursera class :

Don't have a bunch of really short videos, combine them into one longer one.

If there is text or code on a slide, make sure that is in the transcription.

Don't have the dumb popup questions that stop the video and make you find the mouse and click to restart the video. Many of us are listening to the video doing something else, I listen over and over. Sometimes, I have to read the transcription to understand what is being said, so I have to stop, get the mouse, click back up to the slides, press SKIP, etc...

If you have an exam, make sure to later send us the answers - e.g. the code that we were expected to write. This is the weakest and most frustrating part of this class. I was not sure how to some things, in part because I wasn't sure what was being asked, to what detail. Even the class discussions showed we weren't sure what data set to use for what. It seems to rely on peer grading, but most of the responses I got from peers was either completely absent or not useful. But thanks for keeping this relatively cheap.

The course covers basic machine learning algorithms and I do not fully understand why it's ranked as an Intermediate one.

Pros:

You will definitely pick up some new skills after completion this course.

There is simple explanation of a basic idea underlying each algorithm the authors present in the course. It's not hidden in tons of math which is good for beginners and is likely to be bad for those who know something about ML and want to get some deeper understanding.

Cons:

You pay for the course and receive a massive advertisement of IBM services. This shouldn't be tolerated.

You are forced to fulfill a final project using IBM Cloud and Watson studio. So, even if you don't want to work with those tools, it's mandatory for you to waste your time on picking up potentially useless skills.

You might encounter with an error occurring during a signing up on IBM Cloud page. In order to resolve the issue you might need to contact their support team, which is quite annoying, time-consuming and, moreover, your subscription may start another month and you won't be able to suspend it if you want to get a certificate.

Think twice before enrolling into this course. I'd better find an alternative.

At first this class seemed easy to follow, but that was deceptive. While I learned some theory (and some mathematics) behind the algorithms we were meant to learn, there was far too little emphasis on how and when to run the actual code. Normally the labs are a helpful part of these courses, wherein I have the opportunity to actually learn code. Not so with this course.

When I reached the final project for this class, I had no clue how to do what we were supposed to do, as essentially, it had not been taught within the course. I had to seek out other sources in order to actually learn the material and make a lot of educated guesses about what I was suppose to do. I suspect (or hope) that much of this will become easier when I re-take Statistics and some other maths (not course requirements), but that won't make up for the deficiencies in the course. Lastly, the typos and other grammatical errors are extremely distracting and misleading (i.e. "lables" -- do they mean "tables" or "labels"? Who can say for sure!).

The laboratory interface falls short of expectations; its quality does not align with the high standard of the content being taught. In my recent completion of the advanced data analytics course, the Jupyter notebook interface stood out for its excellence, maintaining a clear connection with the course material and lab exercises. For instance, the course taught us how to utilize scikit-learn for the train-test split operation, but the exercise content merely presented the code without the context. While the simplicity is appreciated, there is a noticeable disconnection. If the lab exercises followed a structured approach similar to the course content, such as starting with step 1 - importing libraries and modules, then proceeding to data splitting, model building, fitting, and prediction, it would enhance the learning experience. As it stands, this discrepancy may pose challenges for individuals who are new to machine learning as a subject

From all the courses so far in this certificate this course feels like the least taken care of. Material gets outdated, same typos and bugs according to the forum persist for years, staff replies only very generally. Frustrating issues with online tools they provide when they don't work well for days. I have done most of the labs on the local environment and strongly suggest to everyone else to do the same. Overall I feel like I gained new skills, but it could have been achieved in a better manner. I would not recommend this course to my friends. P.S. And what on Earth is with these forums filled with "Please, review my project"? Any useful threads are drowning among ridiculous requests to do peer review in the course that has automated peer-review system. Jeez.