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Bewertung und Feedback des Lernenden für Maschinelles Lernen von Stanford University

4.9
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169,944 Bewertungen
43,461 Bewertungen

Über den Kurs

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....

Top-Bewertungen

TD

30. Okt. 2021

Thank you very much for the excellent lectures. I am just wondering about the back propagation algorithm. When we calculate the errors backward, why do we use matrices theta instead of their inverses.

CC

19. Juni 2018

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

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76 - 100 von 10,000 Bewertungen für Maschinelles Lernen

von Juan J G P

25. Okt. 2016

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.

von Yashwanth N

19. Juli 2021

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.

Thank you Sir!

von Akyuu F

8. Mai 2019

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

von Hou Z

4. Mai 2019

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

von Nikhil J

18. Mai 2019

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

von Aditya K

18. Mai 2019

It was a very helpful course.

von Mohan K K

17. Mai 2019

Good Course

von Spencer R H

3. Feb. 2019

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

von Ross K

10. Okt. 2015

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.

von Larry C

23. Feb. 2016

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.

von Brian L

25. Mai 2019

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.

von vinod

18. Mai 2019

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.

von Bhargav K

12. Juli 2021

I've learned a lot from this machine learning course. A huge thanks to prof. Andrew for guiding me throughout this course, and also Coursera for providing me with such a platform to learn this course.

von Mekhdi G

23. Dez. 2020

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.

von Saurabh C

10. Juli 2020

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!

von Maksym M

22. Aug. 2018

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.

von トミー ペ

3. Feb. 2019

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.

von Cristian B

2. Nov. 2020

Sorry to give just 2 stars, but the course lacks effectiveness, big time.

I'm a graduate Engineer, even though I'm new to Machine Learning, however iI find this course way too "university-cut", where the theory lesson is fairly quick and simple and mainly focused on demonstrations and abstract concepts, whiles the passage from theory to hand-on implementation is mainly left to the student, who needs to "figure out" how to do it pretty much by himself.

The aspect where this course is failing is the same where traditional academic tuition is failing, and frankly I refuse to learn things exclusively by browsing tons of questions/answers in forums, cause that's a lot of wasted time. Ineffective.

I'm sorry but I can't go beyond 2 stars indeed, as I really can't proceed with such a dispersive learning path.

von Abdelhakim M

11. Juni 2020

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.

von Alex W

13. Dez. 2015

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!

von Dang T G

30. Okt. 2021

Thank you very much for the excellent lectures. I am just wondering about the back propagation algorithm. When we calculate the errors backward, why do we use matrices theta instead of their inverses.

von Ganesh A

16. Mai 2019

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

von Mirko J R

2. Apr. 2019

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.

von Mohammad G

24. Apr. 2020

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.

von pierre c

17. Jan. 2016

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 !