Well explained, at the end of this course you will understand the subject and hold coherent conversations about it. Matlab implementation relatively simple, maybe too much so. Highly recommended course.

There is a lot to say about you Andrew sir but in few words - "Thank you very much for teaching us the ML concepts in such a beautiful manner "

Very instructive course.

Thank you.

It would have been great to get an OCR exercice at the end.

Great course, but the sound quality is quite bad.

Better upgrade from matlab to Python

Good introduction course, giving an overview of machine learning algorithms and some methodology. Off course a lot can be added, but it's a good start for people with little to no knowledge or experience in this field. A few points that could be improved: I would like to have better material support for each section. Marked-up slides are not a great support for reviewing the different sections afterwards.

It would not hurt to provide a little bit more theoretical background and justification when covering the different algorithms. Andrew Ng almost apologizes when going into mathematical equations, but this is fundamental to machine learning.

quiz assignments are rather easy. They could be a little more challenging

I would rather have the programming assignment using R or python than Matlab.

But still a decent course overall I think.

This course needs to be severely updated and fixed. It is mostly kept alive by the amazing community of mentors, in particular, Tom Mosher. Without Tom, I would have gotten extremely frustrated with the weird quirks that come about during assignments. One important piece of advice: if you can do assignments in an Octave environment such as GNU Octave 4.0.3, I'd strongly recommend it (Althought it tends to crash ofter, so save, save, save!!!).

Excellent lessons by Prof. Andrew Ng.

However very poor support. No answers from any mentor along lessons, you should resolve all doubts by yourself.

I had a problem with my ID verification, I was waiting for a long time without any responses.

Also, it's difficult to contact persons who could support you, I tried to contact someone but just found a Bot. Terrible support.

Too easy for people with background in machine learning. But would be a good introductory one if you have zero understanding in machine learning and want to change your career track.

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

The course is not for people with not mathematical backgrounds plus its using matlab.. these days R and Python are more used in the industry for ML. I found to this course via friends that said it's hard but very recommended.. i think there are easier courses online that can deliver the same concepts

.

Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.

Assignments are very messy.

Certificate is expensive!

Too easy and too slow

I would rename this course as Programming Octave with Application to Machine Learning rather that Machine Learning. Once you start the course you will have to focus on Octave rather than on ML topics if you want to do programming exercises. There is no degree of freedom in programming. You are provided with a lot of weird Octave codes which you will have to complete instead of writing yourself from scratch. More than 50% of my time was spent in order to learn Octave and understand (guess!!!!) Octave codes.

So, if you really want to learn ML and try it in practice this course is not for you. However, you could just watch the videos whose level is not more that elementary introduction to ML.

The course is more an exercise in flexing Ivy vernacular than it is actually teaching. The learning curve is too steep to be useful to the majority of potential registrants. You're interested in this course either to (a) learn something about an exciting and ever changing field and/or (b) to have the Stanford logo on your LinkedIn profile. In both cases, move on. The curve is far too steep to be useful or to merit the countless additional hours of background learning the course should have done to bridge the gap.

There are too many mistakes and misleading statements made in the course material. There were a lot difficulties with submitting assignments in order to move forward in the course. I had to give up because I don't have time to be bogged down like this.

The students' comments and discussion would be useful if they can be accessed from within each lesson. I can't make heads or tails of what the discussions were referring to, when they are all clumped together at the course web site instead.

The exercises lead you to the edge of a cliff, then push you off. No guidance. Good luck if you don't already know linear algebra, matrix math, and matlab. I'll be looking elsewhere to learn about Machine Learning. Glad I didn't pay for this course!

Been asked to re-take all assignments *after* paying for a certificate! I wil never pay for a Coursera course again, and I would not recommend my friends to do so

Huge amounts of assumed understanding make this course impenetrable.

This course is definitely not for beginners.

Before I go into why I liked this course so much, let me give a little context on my motivation to taking it. My background is a Bachelors in Math, and 9 years working in finance in a role involving very little computer science or statistics. I wanted a change of industries into the world of Data, for which a significant amount of learning and retraining were necessary; however before just enrolling on and committing to a masters degree, I wanted to answer some questions. Do I enjoy this? Am I able to learn it? Do I want to take this field a step further? Fortunately, the answer to all of my questions was positive.

I have to compare this to the ML module of JHU's Data Science specialisation, which I found rather frustrating as it was too brief to properly go into how the algorithms work. No discredit to the JHU team, I thought the overall course was great and served its purpose, but if you are like me and want to understand what's going on under the hood of these algorithms, this is a superb course. None of the maths is particularly hard, you will need to brush up on some linear algebra, and no prior Matlab is required. Some pretty tough concepts are built up from great simple motivating examples, for me the Neural Network / logic function was the best example of this, and I was extremely satisfied with how I grasped the material. There are enough real world applications thrown in to stay relevant (Data Science is a practical field after all), my favourite was seeing my predictions for number recognition appearing on the screen from the Neural Network I'd just trained appear on screen.

One critique I read of the course which I slightly sympathise with is that the programming assignments become a little like syntax exercises coding an equation into Octave, and thus lose their effectiveness in teaching you. I slightly agree with this and would love to have developed more parts of the algorithms myself, but with the limited time the course has, reading through the code of each of the exercises rather than just clicking through is a decent enough half way step. I would recommend everyone to do this, the point of the course is not just to pass the assignments, but to read around the material a little bit and follow exactly what's going on. That has to be left up to the student.

Overall, I feel like I'm equipped with what I need to get my hands dirty with some datasets to work on my own projects, and give Kaggle a crack. And that's pretty cool considering a few weeks ago I knew pretty much nothing about any of this. Onto the next step in my Data journey!

Before starting this course, I had no previous knowledge of machine learning and I had never programmed in Octave and I have little/no programming skills. This is a 11-week course and so I was not sure if I would make it to the end (or even get through the first week) but I was keen to learn something new.

Positive Aspects: The course is extremely well structured, with short videos (and test questions to help us verify if we have understood the concepts), quizzes and assignments. Prof. Andrew Ng presents the concepts (some very difficult) in a clear and almost intuitive manner without going too much into detail with mathematical proofs, making the course accessible to anyone. The mentors were fantastic and provided prompt responses, links to tutorials and test cases, which all helped me get through the course.

Negative Aspects: Searching the Discussion Board for something specific was no easy task. I would have liked to have known the answers to some of the questions in the quizzes that I got wrong.

What I loved about this course: Learning how powerful vectorization is, it allows us to write several lines of code in one single line and can be much faster than using for-loops. I was wowed several times.

Prof. Andrew Ng is a great teacher. He is also extremely humble and very encouraging. During the course he often said, "It's ok if you don't understand this completely now. It also took me time to figure this out." This helped me a lot. He also said, "if you got through the assignments, you should consider yourself an expert!" and I laughed silly. By no means do I feel like an expert but now I have a basic understanding of the different types of learning algorithms, what they could be used for and more importantly this course has generated a spark in me to use this tool for things that I find interesting and for that I am very grateful. I don't think a teacher has ever thanked me for assisting a class. This is a first-time! So thank you Prof. Andrew Ng and everyone who worked to put this course together. Also, special thanks to Tom Mosher (mentor). My best MOOC so far!

I would definitely recommend this course! I was very impressed by the quality of the lectures. Professor Ng uses the medium very well. He's easy to follow and the content is solid.The assignments were also good. They provide a ton of scaffolding, so you rarely have to write a lot of code, but if you never used Matlab before (like me) and it's been awhile since you've taken linear algebra (also true for me), then "thinking in terms of vectorization" takes a bit of getting used to. I'm really happy that I've been exposed to it, though, and it's pretty impressive how much computation you can express in one or two lines of Matlab.I only had to use the forums once at the beginning to figure out why I couldn't submit assignments. (It turned out that my version of Octave was too new for what the assignments had been tested with.) Once I got that sorted out, I never had to go back there for help, which I thought was a good sign that the assignments were clear and had been through sufficient testing by the staff.It's certainly a bit of a time commitment. I would probably budget at least 5 hours per week. I took a lot of notes, so I paused/rewound the videos a bunch, so it took longer for me to "watch" the videos than the advertised time.Again, the assignments were often not that much code, and I think they started to take me less time as I progressed through the course as I got more familiar with Octave and the style of the assignments. They aren't there to trick you or separate the wheat from the chaff: they're really there to reinforce the concepts from lecture and have you write some code yourself so you have some chance of writing your own code for your own project machine learning project one day.If anything, the assignments provide much more help than I expected. That is, if this were an in-person course where I could go to office hours or whatever if I got stuck, I would expect the assignments to provide less scaffolding and to force you to struggle quite a bit on your own more. (Maybe I just have bad flashbacks to undergrad or something.)