course is a good introduction for people who know 0 about ML (like me). I highly suggest to learn a bit of Pandas, Numpy and Scikit before taking the course, it's highly advantageous.

I found that this course is very directional toward to understanding the basics of MLs techniques using python's libraries and how I can select the best choice for any phenomena.

Great content, peer review process can be a bit painful if someone has submitted a messy, hard-to-trace notebook with a lot of redundant / incorrect cells obfuscating the work.

Quite good in explanation and structured. Can be better by providing more sample study case and comment in each sample code to provide more explanation. Nice course!!! Thanks.

Good course, could have been even better if the coding part was explained in the videos. Instead, it was left to figure out yourself. Still, I appreciate the video lectures.

The course has good explanations of the statistical background and is very practical, but it is unclear how good is the final project, you did it wrong? You will never know.

Easy to understand, step-by-step. The only downside is that there are some bugs found in the lab which cannot be fixed, even many people have already conveyed to the admin.

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.