It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.

Thanks Coursera

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

Perfect foundational overview of the topic with challenging exercises, at least for someone who left university over 20 years ago and has since then not done much with his skills in Linear Algebra ;-)

Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

Very nicely explained the mathematical topics, even for people like me with some phobia regarding large formulas. Useful hands-on experience with MATLAB coding, which I would have had to learn anyway.

Explained well but I was not able to implement it on exercise.

So I switched to other webites .Now I am back here and realized the value of this course

This course could be a nice introduction and overview of the Machine Learning field.

However, the video transcripts are TERRIBLE — do not attempt to find any traces of grammar in them ! After a while I figured there were lecture notes (seriously, why hiding them under Resources ??? some people don't want to or simply can't watch the videos), but some of them lack information needed for the quiz so for some sessions you still have to watch the videos or endure the transcripts anyway.

But MOST OF ALL, the course has an incredible number of (acknowledged) errors, sometimes critical for the programming assignments, and you have to dig into the forum and Resources Erratas to figure them. Given that this lecture has been online at least since 4 years and some people actually PAY FOR IT, I find this utterly disrespectful, hence my low rating.

Furthermore, note that the validation script for ex5 is too permissive : it accepts wrong linearRegCostFunction implementations, which makes the second part of the assignment quite painful to debug…

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts

If this course was in python or R it would have been easier to understand. Octave/MATLAB is not that widely used.

Level of difficulty of lectures is not correspond with level of quizzes. In lectures they are talking about simple stuff and then in quizzes they ask you about details they didn't mentioned. You could deducts this information though. But this is exactly the main problem with this course - for quizzes you should deduct and learn by yourself so much stuff, that videos start to be not worth your time.

This course is absolute garbage. You get no feedback on your quizzes or assignments and the professor is one of the most boring I've ever seen. It's absurdly frustrating to repeatedly fail without any feedback as to why you're failing.

The lectures are clearly from a math perspective, as the prof simply draws what he's talking about on the slides. His hand writing is poor, and he does a lackluster job of explaining what exactly he's doing.

Finally, pure lecture with no notes is almost impossible to learn, as there's nothing to read and study.

I'd rate this course a 1/10, take the course on iTunes from Caltech instead.

However good the material and lectures may be, the use of an outdated version of Octave (which is not Mac-friendly and exceedingly brittle, to the extent where the supplied code requires manual patching in Windows and Linux) is a complete turn-off and makes it nearly impossible to complete the assignments on time unless you're prepared to spend at least twice as much time debugging your setup as doing the actual assignments.

I'll come back when this is done with R or Python.

I‘m a Chinese post-graduate student of Computer Sciense. This class is very useful to me because of it's amazing course videos and the well-designed programming exercises. It is really lucky to have this opportunity to find the course and to finish it. This class will be a footstone for further studying in AI field for anyone who just get started.

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.

An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.

Course content was awesome, gave me lot of insights. If assignments were in Python, it would have helped a lot to improve my skills. Anyways I would recommend this course to a beginner who wants to understand the logic behind the machine learning process. Thank You AndrewNg Sir!!!

Fairly good videos explaining the material, probably worth 4 starts. However, the written support material should be improved. IMHO the video should supplement the written material, i.e. it should be possible to learn the material only by reading. This is not the case, so frequent pausing of videos and making lots of notes is needed if one wants to commit this course to long-term memory.

Have to give a star so I will give it one. Others rate this course highly. I don't know why.

Course states no requirement for knowledge of linear algebra. However this is not really practical and seems disingenuous. I have spent a lot of time re-learning linear algebra.

I have spent much more time on the work than the course states and unless you are currently involved in similar work you probably will too.

I have never received any response to the feedback I provided.

Many times I have been frustrated because the math material is treated casually but then later success on quizzes and assignments are based how well you understand the math. So while the instructor and content can treat the math as casually as they wish, unfortunately, you cannot be so casual.

Overall, this is a great course and I learned an enormous amount of information. The biggest issue I had was the disconnect between the course and the assignments/quizzes. Although they had help sections, because you couldn't ask direct questions about the algorithms/quizzes, if you had a problem, you were basically on your own. (At least that is what it felt like.) For example, if you missed a quiz question and couldn't figure out the answer, there seemed little recourse to find the actual answer. In a couple cases, I decided to just take the 80% on a quiz simply because I had no idea what the answer was.

My first open online course from Stanford University gave me a lot of energy. As my student years are far behind me (I am 76 years old) it was a discovery to become enthusiast in this new area. And building on my career in ICT, this is a surprising extension on the way systems can help us to develop a better life. Professor Ng is very good in offering in a controlled way many insights in the machine learning - now it is time for me to apply my new knowledge!

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days