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Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 166,793
Рецензии: 42,701

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

PT
31 авг. 2018 г.

Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Thanks!!!!!

MS
23 июля 2019 г.

This course is one of the most valuable courses I have ever done. Thank you very much to the teacher and to all those who have made it possible! I will recommend it to all those who may be interested.

Фильтр по:

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

автор: Sergey K

24 янв. 2016 г.

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.

автор: Roman

12 февр. 2021 г.

I would not recommend this course anymore in 2021 since it is almost 10 year old now and it really shows! While essentially a good starter for machine learning, this course spends way too much time elaborating simple and obvious concepts while completely skipping over most mathematical explanations or more in-depth explanations of the presented topics. Furthermore, this course contains a myriad of errors in the presented slides, complete reluctance for any consistency in variable indexing (even in the same equations), painfully obvious editing mistakes, and the English subtitles are utterly useless. Seriously, a machine learning class with a gibberish as subtitles that was probably auto-generated using machine learning is irony at its finest.

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: Mehdi A

24 февр. 2018 г.

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.

автор: Jimmy C

18 мая 2019 г.

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.

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: 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: 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: 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: Priyanka B H

16 сент. 2020 г.

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!

автор: Hu L

14 февр. 2018 г.

Too easy and too slow

автор: Rui L

1 окт. 2018 г.

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.

автор: Reinhard H J

18 окт. 2019 г.

The course content is vastly outdated and superficial.

автор: Subham B

30 авг. 2019 г.

This course is definitely not for beginners.

автор: Pardis J Z

30 июня 2020 г.

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.

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: 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: Seth W

9 нояб. 2020 г.

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.

автор: Kevin H

23 мая 2021 г.

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.

автор: Juan J G P

25 окт. 2016 г.

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.

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.