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Some of the material is out-of-date with respect to current versions of the Python libraries.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The material was excellent however, a lack of downloadable notes and out of date instructions on the use of IBM watson and how not to pay for time on watson need attension to merit a higher rating.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.

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.

It is good overall but missing lots of useful ML algorithms such as boosting algorithms, density and hyrachial clustering algoritms. It also didn't mention about overfitting and underfitting cases comparing with training and test scores.

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.

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.

The instructor is very good and explanation of concepts is very clear.

But the code explaination is not there so we have to search for each keyword on google. Just wanted to have someone to explain code.

not deep enough, many algorithm and model evaluation approaches that wasn't include in this course. But I think for beginner who just get into a Data Science or ML things, this is a good modules

Its Ok. From Theoretical aspect, its good as a introduction. But for Python, this is not like introductory. Python programming is just reading materials. There are no videos for explanation.

So much stuff skimmed, left unexplained. Explanations are very shallow. This course gives you an idea on what you will have to do to tackle ML learning, but I can't say it fully teaches it.

Good explanations in the video, however the complementary notebooks are lacking in depth explanations. The capstone project is underwhelming, as it only includes classifications algorithms.

Was very hard with the algebra. Most of the time they explained the formulas and I was lost. After that they said "This is not mandatory because it is already in the NumPy / SciPy library"