Apr 22, 2017
Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.
Apr 18, 2018
You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.
創建者 Stephen M•
Jun 05, 2019
Jun 06, 2019
創建者 THIERRY L•
Jan 04, 2019
創建者 Saiful I A•
Aug 07, 2015
創建者 Vivek K•
Dec 13, 2018
創建者 Lichen N•
Aug 28, 2019
創建者 Armen M•
Apr 09, 2020
THIS IS A REVIEW FOR BEGINNERS
ADVANTAGES OF THE COURSE
When I remember myself deciding whether or not I should take the course, the questions that concerned me the most were these ones.
1. Since I am a beginner in this field, will the course work for me?
2. Did this course get outdated? (For those who don't know, the professor uses Octave)
3. In the end, will I feel like I can do some Machine Learning projects all by myself?
For those who have the same questions, here are the answers for you )
1. Yes, the course will work for you even if you are an absolute beginner like I was at the time (I did not know any linear algebra), It does get annoying sometimes and you feel a lot of pressure at some point of the course, but a hard-working person can surely get through it. Mentors are active and very helpful if you get stuck on something.
2. This question is a big NO for me, here is why: When you are learning something from the very bottom it is super important to learn the hard way, which is the same as the old way. When you come across an easier path, you understand and grasp it way better. For Octave, many tasks require multiple lines of code, whereas in Python it is just one line. You have to do it at least once with Octave to understand how it works in Python.
3. No, you would not probably be able to start a project on your own, you would need some additional source. But, the point is that you now have a general understanding of what machine learning is, what are important algorithms and what are the key points you should consider when doing project. This is the base that every person should have.
DRAWBACKS OF THE COURSE
Although I loved the course, I could not give it 5 stars because it would have been unrealistic. The lectures of the course have an incredible amount of errors. You should be careful. Although all the errors are covered in the Errata section, it still was annoying to open the section every time when I started a new lecture. to check for errors I am about to see.
Another drawback was the programming assignments. They were not explained well and I almost always had to refer to extra Tutorials made by Mentors.
Special Thanks to Professor Ng and all the Mentors!
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 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 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 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 Sai V P•
Aug 05, 2019
Better upgrade from matlab to Python
創建者 Alexey M•
Apr 10, 2020
Well, this course has at least 3 undeniable cons:
1. It exist;
2. It offers certificate for reasonable and affordable price;
3. It has "Stanford" in title.
Still, it could be improved in many ways.
First of all, it has poor video and audio quality, maybe worst I've personally seen in MOOC. Dear Stanford! Professor Ng is cool, give him room with windows, 1080p camera and microphone! Even less famous educational establishments can afford it.
Second, subtitles are also poor. English is not my native language but I dropped subs in my language after first try. English subtitles also have a lot of errors: many words are garbled with homonyms; I'm lucky to have some background in course theme and without it I would be completely lost trying to understand what's even going on.
Third, I think this and many other courses are suffering from past teaching system and experience. What is classical teaching system? There is lecturer narrating and writing on the board, sometimes showing something; there are students listening and taking notes. Well, still better than "watch your master working, nothing will be explained" method (still present in some cultures), but what century it is? XVII, XIX? We are learning "Machine Learning" via Internet, and watching materials being hand-written in process? Seriously? Even basic HTML skills in this days are enough to show formula, where you can get reminders of it's every part by simply moving cursor on it (Wikipedia is one example). After two weeks break in learning it will be very effective way to remember fast "what's going on, why this formula is so big and what the hell is that squiggle", and learning process will be improved greatly.
Little more HTML effort, and there will be way to live demonstrate curves, planes and how different parameters affect them; it will be possible to let students experiment while learning which is great improvement for learning, memorizing and understanding.
These are just examples, but hopefully my point is clear.
Quizes are too easy, solvable with "hey he just said that" method and some intuition, not require deep understanding.
Programming assignments are well prepared and explained, but programming materials amount is not enough for me.
Thank you professor Ng for your efforts!
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 Daman A•
Mar 28, 2020
The course needs a platform where people can actually apply all techniques independently and learn by way of being graded on their accuracies in prediction. Otherwise the assignments just become a mere copy-paste mechanism of the formulae provided in the pdfs.
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 Vyacheslav G•
Feb 23, 2019
Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave
創建者 Malcomb M•
Jul 21, 2017
Content was OK, but quality of teaching was fair at best -- important points glossed over, many not made clear at all, some simply omitted: Bayes classifiers, decision trees, etc, etc.. Audio visual quality of lectures poor. Ng's onscreen scrawls and voice recording were terrible, and there were many mistakes in graphics. Numerous typographical errors in exercise instruction .pdf's. Exercise text itself (ex__.m files) had numerous "pauses" that failed to instruct the user what he had to do (or not do) next, so you had to carefully examine what followed. If more care was put into exercise construction, the "pause" text in the command window would not just say "Enter to continue" but say what coding action was needed to continue. Obviously a lot of work has already been done on interactivity: Quizzes, online Submit scripts, which for me all worked extremely well. But clearly the course could use a lot of improvement in many aspects. Thus I grade it: C-
May 11, 2018
Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.
創建者 Loftur e•
Sep 17, 2018
Assignments are very messy.
創建者 omri g•
Nov 11, 2015
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
Jul 10, 2019
My feeling is that the author of this course has no idea what is "Machine learning" - I have the impression that he repeats slogans which he does not understand.