Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

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!

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

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).

Andrew Ng is excellent

Exceptional. Best course to start learning Machine Learning! Only one grouse though, the exercises are in Matlab and not in python.

This is a great way to get an introduction to the main machine learning models. The professor is very didactic and the material is good too. I recommend it to everyone beginning to learn this science.

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.

The course is well organised, with cutting edge knowledge ready to use in our information era. And Andrew was really decent with clear illustration and explanations. I really enjoy taking this course!

Very good instruction for machine learning, and also very very good for new comers!!!

It was a great learning experience. All the lectures were in details.

It was a very helpful course.

Good Course

It would be nice if it's taught in either python or R. So I do need to take extra effort to learn octave.

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.

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.

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

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.

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.

This course is a very applicable. Professor Ng explains precisely each algorithm and even tries to give an intuition for mathematical and statistic concepts behind each algorithm. Thank you very much.

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.

Excellent Machine Learning Lessons which need little advanced knowledge of mathematics.

Too easy and too slow

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.

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.

The exercises lead you to the edge of a cliff, then push you off. No guidance. Good luck if you don't already know linear algebra, matrix math, and matlab. I'll be looking elsewhere to learn about Machine Learning. Glad I didn't pay for this course!

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.