The content of the course is great and it's very instructive. Although I think that the certificate approval system should be harder and more inspected by the instructors.

I think IBM Watson site has been updated after this course was last updated. Therefore, there are some differences in the final project guidance videos and actual website.

A really great course with loads of hands on coding experience. But some concepts need to be explained more deeply. Really happy to complete this & receive a certificate !

Good stuff. Useful final project. More in depth research required if you want to actually learn how these algorithms work though - outside the scope of the course I guess.

Good course. I think the final project could be more interesting even but the peer review has to be optimized such that it avoids unfair ratings, either for good or bad.

I think more practice exercise with more variety in difficulty would be really help - as well as links to resources to practice key items (like nesting loops in python)

It is one of the best courses for understanding the basics of Machine Learning. Moreover, it also includes hands on experience with different classifiers on notebook.

There should be assignments every week, not just quizzes. Too easy to pass this way, not enough practice, at least if you already know the basics of machine learning.

Great hands-on practice with many different modeling methods. A fun final project too. Just has a few technical and typo glitches that keep it from a perfect score.

Nicely designed course on ML. All labs are very well structured which help to understand the concepts. Final project is a good test of the lessons learnt in course.

A good and detailed course on machine learning.A very good course for beginners to understand machine learning.

The final assignment was very insightful and helpful.

Loved the way of teaching, the course was worthy and I boosted my skills but the content was not too vast which I was expecting , very easy to complete the course

Good theoretical and mathematical explanation . But the python programming was not explained . We should learn them by our own by reading in the tools in coursera

3.5 stars. Serves its purpose. Skims over much of the important mathematical details. No coding demonstrations other than what you see in the notebooks provided.

The course offers short videos that explains machine learning concisely is quite efficient, and also provides a platform to learners to practice in lab sections.

I found the quality of teaching to be of good standard, however setting up IBM Watson studio and creating a project was very inconvenient and took a lot of time.

Well designed course for clear understanding of ML algorithms. Practical Labs gives you hands on experience to try out various algorithms. A very helpful course.

Good part: This course provide a lot of concrete designed materials.

Not very good part: the content of videos are too simple, without any theoretical analysis.

This course is great but one needs more time to read through other information sources. An in-depth understanding is critical to get the bigger picture of use.

Pros. Good for running code in Jupiter Notebook environment.

ㄴ

Cons. Language Support only English (others are few), and Screen sub overlays presentation text.

Excellent course . Concepts of machine learning algorithm were explained clearly and easily.

The interface with IBM notebook could have been much smoother ,

Would like to say that it would be of great help if we had some more practice on coding. But overall a wonderful course and helped me learn a lot. Thanks!

It was an excellent course. They covered all topics of ML. Practical session was good but need more explanation. That would be very helpful to students.

overall the course is very good and covered the topics in ML, but the coding examples should be explained in a videos to clarify all points in details.

The final project is ill-structured. It is hard to grade because you need to go back and forth in the codes. It makes both doing and grading harder.