22. Dez. 2020
Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.
18. Juli 2021
Amazing really felt that I learnt something substantial. Very happy that I chose this course over others Andrew Ng Sir explained everything very clearly to a required level of depth.\n\nThank you Sir!
von Karl M•
11. Aug. 2017
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.
von omkar d•
30. März 2019
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
von Ali F•
17. März 2021
I want to thank you very much for such a great course in any aspect especially from professor Ng . I just want to suggest that it would be great if there was a final project for the end of the course.
von Bayram K•
17. Feb. 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.
von Mehdi A•
24. Feb. 2018
Too many trainings and assignments without enough practice, exercise and examples. This can be very confusing for a person taking the course for the first time.
von Jimmy C•
18. Mai 2019
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.
24. Juli 2019
This is a very basic course on Machine Learning. The main drawbacks are:
(1) the material is old and not updated to reflect new developments in this dynamic subject;
(2) the course is oversimplified and adapted for students who have never dealt with maths or programming;
(3) the assignments and quizes are, with rare exception, trivial and test students' common sense rather than the subject understanding; for example, you can pass the final quiz at 100% without reading or watching the lectures;
(4) the course is badly maintained: some mistakes in lectures and assignments have not been corrected for years, even though they have been pointed out in the discussion forum countless times.
While the Ng's ML course is arguably better than many other Coursera courses, it is very disappointing that Coursera and Stanford hardly made an attempt to improve it.
von Rune F•
18. Dez. 2016
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.
von zhang w•
2. Apr. 2018
Very nice course,. Give a fundamental knowledge of machine learning in a clear, logic and easy-to-understand way. Suitable for those who has relatively weak background of math and statistics to learn.
von Priyanka B H•
16. Sep. 2020
Loved the course. Andrew Sir explains the intuition behind the concepts really well. Excited to continue with the rest of the courses by him on my way to becoming an AI Engineer.
Thanks a lot, Sir!
von Prabhu N•
28. Mai 2019
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!!!
von Subham B•
30. Aug. 2019
This course is definitely not for beginners.
von Anton D•
24. Apr. 2019
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.
von Herman v d V•
15. Jan. 2019
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!
von Altanai B•
31. Aug. 2020
A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.
von Pardis J Z•
30. Juni 2020
I really enjoyed this course. I learned new exciting techniques. I think the major positive point of this course was its simple and understandable teaching method. Thanks a lot to professor Andrew Ng.
von Hu L•
14. Feb. 2018
Too easy and too slow
von Rui L•
1. Okt. 2018
I would not recommend taking this course any more. (2018)
This course is showing its age and lots of concepts simply doesn't apply any more, considering how fast this field is changing.
von Reinhard H J•
18. Okt. 2019
The course content is vastly outdated and superficial.
von Kevin H•
23. Mai 2021
Programming exercises focus on the topics and provide you with good templates that you can easily fill in so you don't waste your time. Videos are very well done and quizzes are reasonable difficulty.
von Prateek J•
21. Jan. 2019
Exceptional. Best course to start learning Machine Learning! Only one grouse though, the exercises are in Matlab and not in python.
von Armen M•
9. Apr. 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!
von Andrey Y•
1. Okt. 2020
В требованиях к прохождению курса необходимо указать "владение университетским курсом высшей математики" и "математический английский" - без него тут нечего делать, поскольку текстовка на русском языке не совпадает с тем, что говорит лектор ни по смыслу, ни, начиная со второй недели, по времени.
Никаких пояснений по алгоритмам или логике происходящего в курсе нет: вот формула, вот задание. Иди, решай. Курс аналогичен по составу самоучителям по рисованию: "Рисуем круг, рисуем круг побольше, дорисовываем сову."
von Seth W•
9. Nov. 2020
Excellent course, highly mathematical overview of how introductory machine learning models work. Thanks to Andrew Ng for putting together a lot of great material and challenging quizzes and exercises.
von Vishal B•
16. Aug. 2021
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.