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Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 161,734
Рецензии: 41,475

О курсе

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

Лучшие рецензии

JS
16 июня 2017 г.

Everything is taught from basics, which makes this course very accessible- still requires effort, however will leave you with real confidence and understanding of subjects covered. Great teacher too..

QP
24 июня 2018 г.

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.

Фильтр по:

51–75 из 10,000 отзывов о курсе Машинное обучение

автор: Ziwei L

7 дек. 2015 г.

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!

автор: omri g

11 нояб. 2015 г.

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

автор: Tahereh P

26 июня 2020 г.

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.

автор: Prabhu N

28 мая 2019 г.

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!!!

автор: Rune F

18 дек. 2016 г.

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.

автор: Anton D

24 апр. 2019 г.

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.

автор: Herman v d V

15 янв. 2019 г.

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!

автор: Prateek J

21 янв. 2019 г.

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

автор: Subham B

30 авг. 2019 г.

This course is definitely not for beginners.

автор: zhang w

2 апр. 2018 г.

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.

автор: Hou Z

4 мая 2019 г.

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

автор: Nikhil J

18 мая 2019 г.

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

автор: Aditya K

18 мая 2019 г.

It was a very helpful course.

автор: MOHAN K K

17 мая 2019 г.

Good Course

автор: Armen M

9 апр. 2020 г.

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!

автор: Spencer R H

3 февр. 2019 г.

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

автор: Andrey

24 июля 2019 г.

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.

автор: Bayram K

17 февр. 2017 г.

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.

автор: Brian L

25 мая 2019 г.

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.

автор: vinod

18 мая 2019 г.

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.

автор: Ali F

17 мар. 2021 г.

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.

автор: Maksym M

22 авг. 2018 г.

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.

автор: Akyuu F

8 мая 2019 г.

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

автор: トミー ペ

3 февр. 2019 г.

This course was very difficult, coming from a non-math/matlab background, but did teach me a heck ton about the world of machine learning, for which I am eternally grateful. Life got in the way big time, and it took a lot of time and energy to complete the programming exercises. There was also a lot I didn't understand, and I did wish there was maybe another week of getting used to certain concepts, particularly maths issues like double summing. I appreciate that this would complicate things though. I found that I am not geared towards the forums - my learning style involves conversation and not really experimenting on my own (which I can do once I understand a concept). As helpful as the mentors were, only relying on the forums with my time schedule meant that that taking this course dragged on longer than I would have liked. I also got a bit overwhelmed by the lack of centralised information. I know that it would require a complete overhaul to sort such out, but it did make looking up information time-consuming. Nevertheless, I am grateful for all that I learnt, and appreciate that I plunged into the deep end. I don't understand everything, and of course a little knowledge is a dangerous thing, but I know enough to know what to refer to should I ever need ML in my next job. Thank you.

автор: Hu L

14 февр. 2018 г.

Too easy and too slow