I doubt anybody would learn machine learning from this course. It is more of a refresher course than anything significant. There's a lot of information in the videos, but the notebooks are much more difficult. Many of the visualization aspects of the modeling are already completed in the notebooks with little explanation.

The course content is good. But final assignment needs updation. e.g jaccard_similarity_score is

deprecated. It needs to be charged in the notebook. There are less experienced candidates which get this wrong. And it is unfair. They are not expected to know that it is deprecated(That is not the course criteria)

The course is ok for refreshing the essentials For newcomers to ML field the content is too shallow, probably should spend more time on fundamentals Quizzes are not challenging And for some reason thepacing was strange - instead of going with "1 week - 1 module" pace I received deadlines every 2 days or so.

The coding part should be explained as well. The autofilled code makes the learner lethargic and lazy to code himself. I;ve faced this difficulty and I cannot certify that I am 100% sure of what code I've learnt. Please take proper steps in order to teach CODING as well and not only theory.

the level of the course was lower than I thought it will be.. especially comparing to the final assignment.

nevertheless it did give me a strong basic for most of the materials at least to the level I will be able to explain each topic to one who doesn't know nothing about machine learning.

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Some code (particularly relating to density based clustering) is outdated and needs updating.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.