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Вернуться к Машинное обучение

Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 158,435
Рецензии: 40,571

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

AA
10 нояб. 2017 г.

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

PM
13 июля 2019 г.

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.

Фильтр по:

76–100 из 10,000 отзывов о курсе Машинное обучение

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: 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: 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: Ganesh A

16 мая 2019 г.

If it was in python, then it would have got 5 star from me.

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: Mirko J R

2 апр. 2019 г.

Excellent lessons by Prof. Andrew Ng.

However very poor support. No answers from any mentor along lessons, you should resolve all doubts by yourself.

I had a problem with my ID verification, I was waiting for a long time without any responses.

Also, it's difficult to contact persons who could support you, I tried to contact someone but just found a Bot. Terrible support.

автор: Mohammad G

24 апр. 2020 г.

It is a good course that covers essential topics related to Machine learning. But unfortunately, the quality of videos and sound are not satisfying. Besides, there are lots of mistakes in videos, notations, and even in programming assignments. It is time-consuming to check Errata for each week to find out which part has mistakes!! It is even got worse when I was in the middle of a programming assignment and I confused by the WRONG algorithms in the question and notation in the videos. In programming assignment 4, it took a week when I finally realized my mistake occurred because of the wrong algorithm in the videos and the assignment. I found out these problems confused all the students and its evidence is the comments in the forums and responses form mentors.

автор: Ястрембский А Н

1 окт. 2020 г.

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."

автор: pierre c

17 янв. 2016 г.

The course may be great, but the sound of the video is really terrible, this is a big problem for me and possibly to other users, at the point where I decided to stop watching.

Please do something about it !

автор: Subham B

30 авг. 2019 г.

This course is definitely not for beginners.

автор: abbas k

30 мая 2019 г.

so useful

автор: Abdelhakim M

11 июня 2020 г.

The course didn't convince me at all. Practice and applications in real life are in short supply. I missed the art and pedagogy of Trainer.

The certificate is a very poor certificate , no information about contents. No duration of the course is mentioned. It looks like a one day course certificate. This course is 11 Week long. Never again.

автор: Andy M

8 сент. 2018 г.

Huge amounts of assumed understanding make this course impenetrable.

автор: Arunesh G

20 апр. 2020 г.

The BEST course I ever had in my life, even better than a typical classroom based interactive teaching.

This course has the best mix of perfect pace and accurate (to the point) material.

With ample examples, accurate content, greater student-teacher interactions (via programming assignments, quizzes, etc...), and THE BEST TEACHER "Professor Andrew NG", this course is exceptionally the best course one can get in his/her life.

This course is best for beginners as well as intermediate learners.

In the video lectures, not even a sigle second is wasted on off-topic discussion. Each and every second is utilized to the fullest.

In this course, most derivations (complex ones) are skipped, but that is done to help us to focus on the core of machine learning rather than diverging somewhere else. Also, in the end Professor NG teaches about the ceiling analysis which is how and where to focus resources in the development of machine Learning Algorithm, which is not taught in most of the courses I have seen so far.

Overall, this is the best course one can get.

Thanks to Professor Andrew NG

автор: Emmanuel N

6 дек. 2018 г.

Amazing course. I had no idea of programming and my maths were more than rusted, but the way the lessons are taught, made the way a whole lot easier. If you're like me (zero programing and maths), it's no easy task to complete the course. But if you put the right amount of effort, patience and dedication, combined with the great videos and reference material, is totally doable.

автор: Nicholas D

14 мая 2019 г.

Truly an exceptional class. Not often will someone with a deep proficiency in a discipline have the time or incentive to share their insights and teach to others; this class is a rare exception, and given the vital importance of machine learning to the future, I have a great appreciation and debt to Andrew Ng.

автор: Simin L

14 мая 2019 г.

Great class! Should be recommended for every individual who wants to learn machine learning and don't have time or oppotunity to take a class at their own univerisity, this class is a guidance for the basis of machine learning and gives me instructions where to go next. Thank Ng really much.

автор: Yash B

25 мая 2019 г.

This course was very well taught. There was a impressive focus on the basics and fundamentals of each topic. The lecture slides encapsulates the topics well and thus there was no such need of making my own notes which speeded up the learning process ;).

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: Saurabh C

10 июля 2020 г.

One of the best online courses I have attended in a decade. Thank you to Coursera for making this course available. I cannot express my gratitude enough to professor Andrew Ng for this awesome course!

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: Hejmadi P B

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: claire.hou0701@gmail.com

18 мая 2019 г.

sehr gut!

автор: Alexander C

16 июля 2020 г.

This was a great course, and I highly recommend it! Andrew Ng made me feel like he's my machine learning pal. I can see why this course is so popular.

I docked it a star because the assignments could really use an update. The work flow for completing them includes consulting multiple documents of (sometimes contradictory) instructions as well as errata documents, tutorial posts, and discussion threads. It's too much and when your script isn't working it makes it difficult to know whether you made a mistake or if maybe there's some updated note that you missed. If all of the assignment notes were just consolidated into one document, then five stars for sure!

автор: Jerome T

6 мар. 2019 г.

I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.