This course is a great introduction for people who have a background in Python and mathematics, but from a personal perspective, it should pay more attention to the details of the machine learning algorithms and special cases and do more practice using harder, more inconsistent use cases.

Some code (particularly relating to density based clustering) is outdated and needs updating. Also, quizzes with multiple-choice answers such as "none of the above" or "all of the above" need to be pinned to the last choice, rather than randomly placed, which creates lots of confusion!

Very demanding, that is great!!

It is not updated, therefore many instructions are incorrect and instead of 1 hour, one can use 2 or 3 days.

The system Watson Studio is not working all the time. I lost many lines in my final project because it stoped before I could save the changes

The only OK course in the specialization. I found that the intuition/concept behind various algorithms was explained quite well. The mathematics, on the other hand, were basically skipped. And as always, the assignments are sadly pretty much "copy and paste from the examples".

Good course and quite relevant. However, the project was not gearing up for the final Capstone project. I did not feel the skills I gained from this course set me up to succeed with my Capstone project. I felt like I was still in the dark running any kind of machine learning

This course is a good start. There was not enough assistance with completing the course work. I waited 2 weeks for help and had to send in my assignment incomplete. I wish there was at least one session where students could get their questions answered in real time.

Some of the material is out-of-date with respect to current versions of the Python libraries. There are a lot of typos in the material. In the final assessment, the instructions were quite vague regarding what information should be included in the submission.

The material was excellent however, a lack of downloadable notes and out of date instructions on the use of IBM watson and how not to pay for time on watson need attension to merit a higher rating.

Lastly, facilty response to questions was outstanding!

It go through many kinds of machine learning with only simple sample. it doesn't seem like I can earn some job-ready skill after taking this course. The introduction is good, but the content are just too simple to help us deal with real problem.

It is an overwhelming course.. really it is packed with knowledge.. yet I with that in the video the instructor explain more in the code.. the theoritical knowledge is understandable but when coding comes.. things getting little bit difficult.

It is an intermediate level course that would be great if it could help or remind you of the uses of the libraries and the steps to not fall short in the explanations in their real application in Python. Otherwise, very good and thank you.

Explained basic methods of machine learning but could have provided more guiding information on the final project that encouraged learning and helped us complete the project efficiently but also compel us to explore the methods thoroughly.

Loads of typos/spelling mistakes throughout, some contradictory statements in the quizzes that need to all be ticked, some questions are unclear... Overall the content isn't bad but the entire course needs to be spellchecked and reviewed.

Good survey material for those unfamiliar with statistical concepts, but the training material is incomplete, misleading, RIDDLED with spelling/technical mistakes, and only the forums address the methods to submit homework correctly.

Not too many explanations for the assignment, only 2 rows which are supposed to tell us exactly what we have to do. This is why everyone ended up creating very different things, which made it harder when reviewing their work.

Great course but the final assignment was very fiddly with standard libraries not being uploaded properly in the Watson lab notebook provided. There was no option to use local environments to mitigate this. Hence 3 starts

I think that the Machine Learning Models taught were explained really well In theory to help understand what we are doing. However, there is not much explanation to the syntax of the models which could be elaborated on.

It's good course if you have not heard anything about Machine Learning, however I would like that teach important techniques such as neural networks, PCA because they are used many times in different jobs and studies.

The course was very intense and it was not clear what was wanted from you(i.e. the scope you're expected to know for the exams)

The quizzes are vastly different from the final project and don't prepare you for it.

The assignments covered only the basics, without aspects like overfitting detection, hyperparameter tuning, ensemble learning, clustering, dimensionality reduction, missing data imputation, and cross validation.

I got a great understanding of the concepts but, there should have been more videos related to the implementation(coding) part. There was just once use of Third-party tool for every module and nowhere else...

Content was good, a bit shallow on some aspects (didn't cover many ML techniques, was light on SVM content, etc.). But the quizzes were too easy and didn't properly test technical aspects of the course.

The sections on Clustering and Recommender Systems were difficult to follow. It would have been ideal if they had more in-depth video explanations or if the contents in the lab notebooks was simplified.

It is a really good course for understanding theories and covers vast topics! The concept were explained very nicely but it lacked proper mathematical working of algorithms or deep intuition about them.

There is more theory than practical examples and exercises. The final project is nowhere near the actual course syllabus. No ML algorithm is taught in the course. But you ask them in the final project.