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Вернуться к Машинное обучение

Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 161,768
Рецензии: 41,487

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

JP
24 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.

YN
18 июля 2021 г.

Amazing really felt that I learnt something substantial. Very happy that I chose this course over others Andrew Ng Sir explained everything very clearly to a required level of depth.\n\nThank you Sir!

Фильтр по:

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

автор: Hu L

14 февр. 2018 г.

Too easy and too slow

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: Ross K

10 окт. 2015 г.

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.

автор: Larry C

23 февр. 2016 г.

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.

автор: Alex W

13 дек. 2015 г.

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!

автор: Reinhard H J

18 окт. 2019 г.

The course content is vastly outdated and superficial.

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: priyanka 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: Ganesh A

16 мая 2019 г.

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

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: 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: 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: Andy M

8 сент. 2018 г.

Huge amounts of assumed understanding make this course impenetrable.

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: Mekhdi G

23 дек. 2020 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: 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: Caleo M S

23 мая 2020 г.

Um curso incrível com uma ótima didática e exercícios que realmente estimulam o que foi aprendido em aula. Sem dúvida é a melhor fonte de conhecimento para adentrar no mundo de Máquina de Aprendizado.

автор: abbas k

30 мая 2019 г.

so useful

автор: Anand R

20 нояб. 2017 г.

To set some context: I am a graduate (PhD) in Computer Engineering from the University of Texas at Austin with over 10 years of experience in both academia and industry. My goal in taking this course was to learn the basics of Machine Learning, and understand what the current excitement about ML and AI is all about. I dedicated 3-4 hours every week, over the last 12 weeks, towards learning this course — and watched all the videos, reviewed all the lecture .pdfs and completed all the project assignments and all quizzes in the course on time.

About the course: This is one of the best courses I have taken (and I have taken more than 10 courses on coursera, edX and Udacity). Dr. Andrew Ng needs no certificate of approval from anyone. He is clearly a wonderful teacher, and I felt I struck a chord with him. There are few people who can explain complex concepts clearly without over-simplifying. Some people don’t have the ability, and often those who do, don’t care enough. The difficulty often lies finding that boundary — the boundary where the complexity of a computation or a problem or a strategy can be abstracted out (with a black-box, or an analogy) and a student can make progress in thinking about the problem without getting bogged down. Dr. Ng does that very well in several places and my deepest respects to him for doing that.

Clearly, Dr. Ng is a pactitioner in the field. The material was very well structured, very well paced and presented in bite-sized modules. The project assignments were both challenging and quite realistic. I feel a tremendous sense of confidence having completed this course, and I hope to try out some ML challenges on the web in the near future.

Last, but not the least, I cannot appreciate Dr. Ng more for the effort and dedication he has put into the subject and into his teaching. I felt a touch of nostalgia as the course ended suddenly with the last video (which was very moving, btw) and there was no NEXT button to click on. Being an educator myself, I know it takes a LOT of time and effort in developing a course. After completing this course, I felt I owed it to Dr. Ng. to purchase the course. I feel proud and happy to be certified as his student.

Thank you, Dr. Ng.

Thank you coursera.

автор: Melinda N

4 сент. 2015 г.

Before starting this course, I had no previous knowledge of machine learning and I had never programmed in Octave and I have little/no programming skills. This is a 11-week course and so I was not sure if I would make it to the end (or even get through the first week) but I was keen to learn something new.

Positive Aspects: The course is extremely well structured, with short videos (and test questions to help us verify if we have understood the concepts), quizzes and assignments. Prof. Andrew Ng presents the concepts (some very difficult) in a clear and almost intuitive manner without going too much into detail with mathematical proofs, making the course accessible to anyone. The mentors were fantastic and provided prompt responses, links to tutorials and test cases, which all helped me get through the course.

Negative Aspects: Searching the Discussion Board for something specific was no easy task. I would have liked to have known the answers to some of the questions in the quizzes that I got wrong.

What I loved about this course: Learning how powerful vectorization is, it allows us to write several lines of code in one single line and can be much faster than using for-loops. I was wowed several times.

Prof. Andrew Ng is a great teacher. He is also extremely humble and very encouraging. During the course he often said, "It's ok if you don't understand this completely now. It also took me time to figure this out." This helped me a lot. He also said, "if you got through the assignments, you should consider yourself an expert!" and I laughed silly. By no means do I feel like an expert but now I have a basic understanding of the different types of learning algorithms, what they could be used for and more importantly this course has generated a spark in me to use this tool for things that I find interesting and for that I am very grateful. I don't think a teacher has ever thanked me for assisting a class. This is a first-time! So thank you Prof. Andrew Ng and everyone who worked to put this course together. Also, special thanks to Tom Mosher (mentor). My best MOOC so far!

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: Muhammad S A

22 апр. 2021 г.

I am an experienced ML engineer and I have previously taken many different machine learning courses covering various sub-topics in detail and worked on multiple ML projects. This one covers the base theory the best. In practical terms, a lot of companies won't use MATLAB and I personally like Python more. That language issue is about the only shortcoming but I understand that it would be better for a beginner to use MATLAB instead.

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.