Jul 16, 2019
The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).\n\nAndrew Ng is excellent
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.
von Prakash M•
Jul 14, 2019
This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.
von Prateek J•
Jan 21, 2019
Exceptional. Best course to start learning Machine Learning! Only one grouse though, the exercises are in Matlab and not in python.
von Hou Z•
May 05, 2019
Very good instruction for machine learning, and also very very good for new comers!!!
von Nikhil J•
May 18, 2019
It was a great learning experience. All the lectures were in details.
von Aditya K•
May 18, 2019
It was a very helpful course.
von Prabhu N•
May 28, 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 Fernando A H G•
Jul 21, 2019
Exceptionally complete and outstanding summary of main learning algorithms used currently and globally in software industry. Professor with great charisma as well as patient and clear in his teaching.
von Eric J•
Mar 27, 2018
Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.
von Abdul Q•
Mar 03, 2018
An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.
von Kothala M K•
May 18, 2019
von Harshal M•
Mar 25, 2019
If this course was in python or R it would have been easier to understand. Octave/MATLAB is not that widely used.
von Herman v d V•
Jan 15, 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 Brian L•
May 25, 2019
There's one saying in Chinese that says "一日為師，終身為師" which means once being someone's teacher, even just one day, you're the teacher for the rest of his life. Thank you for all your efforts and I really appreciate it. I'll keep working on Machine Learning and hopefully one day I can do the same contribution to the human society as you did.
May 18, 2019
Explanation was very good and assignment helps us to understand the real picture. The way course is planned along with octave exercise, Graphs and visualization of data (X,Y) is very good. Very good course who is starting the Machine learning from scratch.
von Quoc-Viet P•
Jun 25, 2018
This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.
von Maksym M•
Aug 22, 2018
So much like it. It gave me starting push in this interesting topic. And one important thing that after this course I figured out I need to continue dive into machine learning.
von Akyuu F•
May 08, 2019
Excellent Machine Learning Lessons which need little advanced knowledge of mathematics.
von Spencer R H•
Feb 03, 2019
It would be nice if it's taught in either python or R. So I do need to take extra effort to learn octave.
von Marcin K•
Mar 02, 2018
The course covers a lot of material, but in a kind-of chaotic manner. There is a lot of math, so if you're not familiar with linear algebra you may find it really difficult. Personally, I don't quite understand the approach. The goal of this course seems to be to teach people how the algorithms work, and if so - there is just enough math, for the students to get lost, but not enough of it to truly understand what's going on internally in the algorithms.
Also, the vectorization techniques of the provided formulas is not quite well explained, and it's left to the students to figure it out. This lead me a lot of times to trial and error approach, when I was just trying different approaches until something worked, but it was still hard for me to understand what really happened. Oftentimes I found myself spending more time on trying to understand how the matrices and vectors are being transformed, than actually thinking how the algorithm works and why.
Another thing is that after finishing the course, you have almost ZERO experience with real-world tools you're supposed to use for real-world projects. I'm thinking TensorFlow, R, Spark MLib, Amazon SageMaker, just to name a few.
On the bright side, the course teaches several general good practices like splitting the datasets to training, cv and test. It also explains very well how to work with different ML algorithms, how to monitor they are "learning well", and how to fine-tune their parameters or tweak the inputs, in order to gain better results.
The course ends with assuring students that their skills are "expert-level" and they are ready to do amazing things in Silicon Valley. That is obviously not true for the reasons I already mentioned (e.g. lack of tooling experience). I see this course as a starting point for anyone who seriously wants to go into ML topics, and to actually understand at least some of the internals of the 3rd party libraries he'll end up using. But don't think you'll end this course with any practical knowledge, or that you'll be ready for real-world problem solving.
von Sergey K•
Jan 24, 2016
Level of difficulty of lectures is not correspond with level of quizzes. In lectures they are talking about simple stuff and then in quizzes they ask you about details they didn't mentioned. You could deducts this information though. But this is exactly the main problem with this course - for quizzes you should deduct and learn by yourself so much stuff, that videos start to be not worth your time.
von Rune F•
Dec 18, 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 Mathew L•
Sep 25, 2015
This course is absolute garbage. You get no feedback on your quizzes or assignments and the professor is one of the most boring I've ever seen. It's absurdly frustrating to repeatedly fail without any feedback as to why you're failing.
The lectures are clearly from a math perspective, as the prof simply draws what he's talking about on the slides. His hand writing is poor, and he does a lackluster job of explaining what exactly he's doing.
Finally, pure lecture with no notes is almost impossible to learn, as there's nothing to read and study.
I'd rate this course a 1/10, take the course on iTunes from Caltech instead.
von Rui C•
Dec 12, 2015
However good the material and lectures may be, the use of an outdated version of Octave (which is not Mac-friendly and exceedingly brittle, to the extent where the supplied code requires manual patching in Windows and Linux) is a complete turn-off and makes it nearly impossible to complete the assignments on time unless you're prepared to spend at least twice as much time debugging your setup as doing the actual assignments.
I'll come back when this is done with R or Python.
von Rajdeep D•
Mar 31, 2018
Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days
von Marius N•
Oct 31, 2017
Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.