Jun 15, 2016
Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.
May 19, 2019
This is the best course I have ever taken. Andrew is a very good teacher and he makes even the most difficult things understandable.\n\nA big thank you for spending so many hours creating this course.
創建者 Mohd F•
Nov 08, 2018
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 "
創建者 Jerome T•
Mar 06, 2019
I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.
創建者 Mehdi E F•
Mar 19, 2019
Very instructive course.
It would have been great to get an OCR exercice at the end.
創建者 Nils W•
Mar 23, 2019
Great course, but the sound quality is quite bad.
創建者 Saideep G•
Apr 09, 2019
Very well made, well paced. Better than majority of college courses. Some errors do pop up midway through the course that should be addressed. It can be frustrating to push through these issues sometimes but they are the only thing keeping from 5 stars.
創建者 MAHESH Y•
Apr 09, 2019
it is one of the best course for beginners in machine learning, the only thing it lacks is its python implementation. If there is the python implementation of this course then no other course is better than this one
創建者 Doreen B•
Jun 09, 2019
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.
創建者 Sai V P•
Aug 05, 2019
Better upgrade from matlab to Python
創建者 Eric S•
Jun 06, 2018
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!!!).
創建者 Shitai Z•
Nov 19, 2018
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.
創建者 Mirko J R•
Apr 02, 2019
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.
創建者 トミー ペ•
Feb 03, 2019
This course was very difficult, coming from a non-math/matlab background, but did teach me a heck ton about the world of machine learning, for which I am eternally grateful. Life got in the way big time, and it took a lot of time and energy to complete the programming exercises. There was also a lot I didn't understand, and I did wish there was maybe another week of getting used to certain concepts, particularly maths issues like double summing. I appreciate that this would complicate things though. I found that I am not geared towards the forums - my learning style involves conversation and not really experimenting on my own (which I can do once I understand a concept). As helpful as the mentors were, only relying on the forums with my time schedule meant that that taking this course dragged on longer than I would have liked. I also got a bit overwhelmed by the lack of centralised information. I know that it would require a complete overhaul to sort such out, but it did make looking up information time-consuming. Nevertheless, I am grateful for all that I learnt, and appreciate that I plunged into the deep end. I don't understand everything, and of course a little knowledge is a dangerous thing, but I know enough to know what to refer to should I ever need ML in my next job. Thank you.
創建者 Jerome P•
Mar 30, 2018
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.
創建者 Ivan Č•
Feb 24, 2016
Certificate is expensive!
創建者 Saleh a h•
Jul 14, 2017
good so far
創建者 Loftur e•
Sep 17, 2018
Assignments are very messy.
創建者 Hu L•
Feb 14, 2018
Too easy and too slow
May 11, 2018
Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.
Feb 19, 2018
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
創建者 Manik j•
Sep 26, 2019
This course is good but it cannot be able to clear the very basic knowledge of the student and also donot they tell to how to code the things
創建者 Larry C•
Feb 24, 2016
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.
創建者 Ross K•
Oct 10, 2015
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.
創建者 Andy M•
Sep 08, 2018
Huge amounts of assumed understanding make this course impenetrable.
創建者 Subham B•
Aug 30, 2019
This course is definitely not for beginners.
創建者 Bayram K•
Feb 17, 2017
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.