Mar 03, 2018
My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.
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.
par Saiful I A•
Aug 07, 2015
par Vivek K•
Dec 13, 2018
par Lichen N•
Aug 28, 2019
par 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 Did this course get outdated?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!
par 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.
par 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.
par 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
par 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.
par 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 "
par Mehdi E F•
Mar 19, 2019
Very instructive course.
It would have been great to get an OCR exercice at the end.
par Nils W•
Mar 23, 2019
Great course, but the sound quality is quite bad.
par Sai V P•
Aug 05, 2019
Better upgrade from matlab to Python
par 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!
par 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!!!).
par 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.
par 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.
par Vyacheslav G•
Feb 23, 2019
Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave
par 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.
par Loftur e•
Sep 17, 2018
Assignments are very messy.
par 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.
par Subham B•
Aug 30, 2019
This course is definitely not for beginners.
par Harry E•
Oct 04, 2017
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!
par Melinda N•
Sep 04, 2015
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!