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Bewertung und Feedback des Lernenden für Maschinelles Lernen von Stanford University

4.9
Sterne
167,051 Bewertungen
42,765 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

AS
22. Okt. 2020

This course is awesome. Learning material is easy to understand. Before this course i tried to read some technic literature about ML and it was scary, but in this course i enjoyed by learning process.

KH
22. Mai 2021

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.

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76 - 100 von 10,000 Bewertungen für Maschinelles Lernen

von Aditya K

18. Mai 2019

It was a very helpful course.

von Mohan K K

17. Mai 2019

Good Course

von Armen M

9. Apr. 2020

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!

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 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 Altanai B

31. Aug. 2020

A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.

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 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 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 Akyuu F

8. Mai 2019

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

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 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 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 Vishal B

16. Aug. 2021

My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.

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 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 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 !

von Andy M

8. Sep. 2018

Huge amounts of assumed understanding make this course impenetrable.