Had higher expectations. Concepts not well and clearly explained. Notebooks bugged (we are actually warned about it), but even so not so interesting. Plan of the Course not so rational: why include the one section about model parameters on its own, rather than for each model.

I give it a 3 as the Instructor is smily and engaging, but it's a 2.5 mark (I have done another ML MOOC on another concurrent platform about the same topic, and the quality was much higher)

Excellent course, I was looking for a course which didn't explore advance math or go into the specifics of a particular ML method but which focuses on the main differences among then and teach about the whole process of M, this is the best course for that.

Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.

Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.

Very important concepts about supervised ML are presented. Really liked the course but a little stressed about the graded quices, even though I enjoyed very much.

Great course but less in-depth knowledge about each of the hyper parameters and under the hood view of Algorithms.But excellent. Thanks!!!!!!

A great short capsule course to get overall bird view on Supervised learning. Much needed one for both practitioners and new beginners.

although the course felt a little hurried, I found the course and the instructor to be very engaging. I look forward to learning more

It's a nice course for those who likes to learn the supervised machine learning algorithms with practical experience.

The explanation of the topics are easy to understand due to the dynamics of theory, practical exercises and quizzes.

The whole specialization is extremely useful for people starting in ML. Highly recommended!

Easy and engaging. But would loved it more if some more coding examples were given.

Nice course! Good idea to add more practice with Jupyter Notebooks!

Great course! I received so much useful information from AMII.

Excellent.

Teach you practical stuff that other courses don't.

A good refresher on some commonly found learning algorithms.

Good content and nicely delivered!

Explained things clearly

Many useful information but need some more explanation, overall awesome

More maths to explain the underlying concepts will be good!!

week two was heavy

This course covers lots of important ideas and knowledges for Machine Learning practitioners. It is definitely nice to deal with topics such as grid search or scikit-learn, but I think the course only covers these topics in a nutshell, it is more superficially discussed. If you are interested in Machine Learning, you should definitely bring your own motivation to dive deeper into those topics.. Also, Dr. Koop speaks very very fast though.. I attended courses by Andrew Ng, his courses provide a way better comprehensibility for listeners. The notebooks are a bit weird, very easy to understand and are hence not challenging. If you really want to understand the algorithms deeply, I don't think this course is the right one. But all in all, I completed the course, but I don't think I was able to understand everything by taking the course only.

The content was good but the videos went too fast and too much theory was involved. For a beginner it was too much to take. I was expecting some Practical and programming aspects in Quizzes and tests but that is okay. Overall a good experience

Good coverage of the topics in supervised learning. However, lacks depth in some of the concepts.

good