the content given was good.

Good overview of machine learning as a subject. Introduces terminology and Andrew NG is very clear about what aspects of his lectures are necessary and what aspects are a deeper dive for enthusiasts.

Excellent Course! It would've been better if we used Python instead of Octave/Matlab.

Cours intéressant

should focus on course content guidelines, there are error in formula in the course.

the course was amazing. Yet, I didn't like coding in OCTAV

You can start it easily. It requires you only basic knowledge of linear algebra, so it won't make you hesitate to study.

On the other hand, it would be much better if it offered more complicated examples for practice or more detailed explanations of models like motivation of using them or what is happening under it.

Still it was really great lecture since it offers very well designed exercises and good explanations.

Mentors were excellent. Tutorials for programming assignments were excellent. Videos were very good. PDFs with the programming assignments were sometimes behind the times, too many unresolved errata.

Nice

Great introduction, but I am not sure that I can resolve real problems :)

It's a great introduction, and covers many useful topics. Using MATLAB for ML makes code look more elegant if you understand MATLAB, but it's not very widely used outside of physics so using it for teaching Machine Learning is questionable. I also took particular issue with Andrew Ng's smug remarks about how anyone who takes this course is now "an expert in machine learning" or that 3 weeks in you already know more than many people doing ML in silicon valley. Statements like this are clearly false. I'm literally in silicon valley and there are no ML jobs to be had for anyone who doesn't have a dedicated degree and/or at least 3 years experience with whatever software they are using. And they never use MATLAB.

Amazing content. I think this is a must-learn course to start your journey towards the world of Machine Learning and Artificial Intelligence.

Good course. I wish there was a bit more derivation/depth but it exceeded my expectations and I learned a lot.

This course goes so much depth into the fundamentals of Machine Learning and its concepts. It is very useful to get deep understanding of the concepts. But, I will suggest making a few improvements in this course as it is too old and audio qualities are very low. and also, having some Python-based implementation will lead some industry-friendly applications

Excellent curriculum. struggled with quiz wording; on many occasions, I simply did not understand the question as worded. Octave on OS X fiddly... The "pause" primitive in the MacPorts version of Octave didn't work and it was necessary to write an override;

The UX "course flow" of the Coursera material is inconsistent. For example, moving on from quizes requires a different navigation pattern than the rest of the course.

The exercise environment is good and the exercises are excellent; nonetheless, a large amount of preprocessing and "heavy infrastructure lifting" is done for the student. While I understand the practical considerations regarding avoiding distracting obstacles in order to focus on the core concepts; a collateral result is many students could vastly under appreciate how much preprocessing has been done on their behalf and take this for granted. Perhaps the student should be advised to understand how much preprocessing was done and how they need to understand the scope and effort involved; at least so they know to include budget and resources for this kind of Data Prep wok that ay be required;

2nd round to get a certification for career development.

Very good introduction with some deeper dives where appropriate. The course notes are often hand written so taking your own notes in parallel is recommended.

While the course focuses on implementation of ML algorithms and their implementation, it would be very nice to have an explanation of the underlying mathematical concepts for these methods.

Also, it would be nice to have a big project, or a project that is done in stages as the weeks progress, towards a bigger real life application like the Photo OCR

It was one of the best courses I've had. The practical tasks were sometimes quite challenging yet quite narrow - you have to complete only a small part of the whole excercise I'd prefer to do the whole by myself.

This is put together well, but mostly covers techniques that have become less popular in favor of DNNs.

Very Helpful to understand to see how the graph and calculation to get accurcy.

Very Good course. Helps you get in touch with the mathematics side as well as helps to develops a good intuitions for the algorithms.

the best course for beginners

If they have used Python instead of octave I would have given a 5 stars.

But the theory is deep and solid. Even though the course is back from 2011, The content is still valid today.

Its very good course to start with, but the only disadvantage of this course is that it uses Matlab not Python