Really good and easy going course. It gets us familiar with a lot of useful techniques in ML, which surely will be explored deeper in further courses in the specialization.

You can get the overall idea of how different algorithms perform using inbuild libraries. You will not have to learn the underlying mathematical concepts for this course.

It provides a good general insight into different implementations of machine learning.

The only downside is the proprietary and commercial licensing of the used algorithm.

This course start from problem. So this great to motivate the content. However, there are lot of confusion questions that lead to miss understand the exercise problems.

An awesome course to get a basic idea about various machine learning techniques. Would've been great if instructions for scikit learn were also provided in the videos

Very clearly explained, easy-to-follow pace and great examples.

would have been absolutely great if there were references to do the same exercises using pandas/scikit

Great Course, I really enjoy it. Compared with other machine learning courses, I think this one is really a good start, for its using of examples close to our life.

Last section was tedious. Access to a year long graphlab licence was nice though especially since I graduated as an undergraduate in 2014 and am no longer a student.

Very good course since it give you a high level view on Machine Learning. It would had been useful to have complementary readings and examples using other libraries

Very much interesting and deep. Gives hands on experience with real life examples and with basic knowledge. I would definetly recommand this course to new learner.

I really enjoyed the material. I think the course should focus more on open source tools. Some of the quizzes are rather badly worded and could use some retooling.

The course is great, you do learn a lot of the general concepts. I wish they would use general python packages instead of graphlab, but this is not a deal breaker.

The course was taught well, however I thought there could have been a few more videos with more details. Although I understand that it's really an overview course.

Really interesting course that gives a great introduction to Machine Learning. Might be a little too much for someone with no programming or statistics experience.

Libraries are old and no support . Though I somehow worked it out.

But man explanations are so good and the professors are nice too.I like how they make us laugh .

Really good introductory course. Sadly the code in the video is not up to date and there are points were the questions are quite vaguely formulated in the quizes.

The course is great for beginners, I would have appreciated a more detailed explanation of statistical models used rather than using readymade graphlab functions.

Good for the beginners to learn Machine Learning and Neural Networks in detail. This course also includes hands on sessions/ assignments which helps in learning.

Many basic ML concepts were touched upon - the assignments, especially in Parts 1-4 were very easy and required very little brainstorming :) - hence a star less.

The course is very comprehensible, even for not so technical people. The materials are good and the lecturers are able to explain complex concepts to everyone.

This course has provided a very good insight of what are the tools that are applied in machine learning and how fast they are from the already existing tools.

Good introductory course, some videos require more than one time to review them and even doing external research on the topics to understand well the concepts

The lectures are substantive, although they tend to be too short in duration. The python application component is very applicable to work-related situations.

Excellent course, very charming professors, accessible even for people with limited math/coding backgrounds. Some course material needs to be updated, though.

The course was wonderful. Had a great time learning with doing hands-on on real-time data. But there could have been a more detailed algorithm's explanation.