could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice

Excellent course for beginners to data science field. Would have been better if the final project also included flavor of other ML methods such as Regression, Clustering or Recommender Systems.

The theoretical part is well done and very interesting, but at the end of the course the explanation regarding the use of Watson Studio for the exercise and the final test is quite misleading.

This course walks us through the fundamentals of machine learning methods. The capstone project is very useful for those who have previous knowledge of machine learning and Python programming.

I think PCA would've been a very useful clustering method to teach. AUC are a great way to measure the effectiveness of a logistic regression algorithm, it would've been useful to learn here.

Very nice course with very less time .

But i though there should be some mathematical explanation in detail what i observed there is lack of mathematical explanation.. overall course is good

That course was very instructive and provides a very good start in the field. The instructors could dive a little bit into more into technical details, or give more examples of algorithm.

its a great journey along with coursera family and very thankful of sponsering such good course.i have enjoyed alot and learning so much from you.its my pleasure to i done this course!

Great course if you have a previous knowledge in ML and python, because there's not much deepening into every subject. Useful labs to understand the coding, but with not much difficulty

Good course to introduce us to the fundamentals of ML. Some of the routines used are becoming deprecated in the notebooks, and there are quite some spelling errors within the notebooks.

The modules and in particular the Labs are good background, pay attention to the optional labs because you are going to need it to complete the final project! :) really good learning

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.