Chevron Left
Zurück zu Maschinelles Lernen

Bewertung und Feedback des Lernenden für Maschinelles Lernen von Stanford University

170,043 Bewertungen
43,486 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....



22. 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.


18. 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.\n\nThank you Sir!

Filtern nach:

326 - 350 von 10,000 Bewertungen für Maschinelles Lernen

von José M V G

9. Feb. 2022

Great course for grasping the fundamental algorithms of Machine Learning, even though the assignments are quite outdated in my opinion and don't align with the state of the art in programming.

von Moto G

8. Nov. 2018

There is a lot to say about you Andrew sir but in few words - "Thank you very much for teaching us the ML concepts in such a beautiful manner "

von Mehdi E F

19. März 2019

Very instructive course.

Thank you.

It would have been great to get an OCR exercice at the end.

von Nils W

23. März 2019

Great course, but the sound quality is quite bad.

von Sai v P

5. Aug. 2019

Better upgrade from matlab to Python

von Stefano B

10. Sep. 2016

Despite I guess the course has a pretty good coverage of the ML basics, it is definitely just an introductive class. In particular I was surprised by the low quality of the material.

The following are my notes and suggestions:

-- I found the lectures highly redundant, with many unnecessary repetitions

-- using a vector notation (like an arrow or a simple line on top of the letters) throughout the course would have make formulas much more readable

-- too much hand writing on the slides while talking: a better set of slides with blocks of text shown at the right moment would be much smoother and readable

-- very, very poor video editing (many times it's clear some parts of the videos were meant to be cut!!)

-- the desire to create a format suitable for people with a scarce algebra preparation lead to use not the appropriate terminology, which would be more correct and easier to understand. Just realize that ML is basically applied math, and without a good math knowledge it is almost pointless to approach the subject

von I S

1. Apr. 2018

There are now so many pages with errata and multiple programming exercise tutorials that I would expected that the course material would have been updated long ago. Book publishers eventually stop making errata sheets and instead make new editions, I suggest you do the same if you want to sell this for $80. I got so confused by one of the programming tutorials in the discussion forum ( and didn't see that there were other programming tutorials under the long list of errata sheets in the ressources section, that I ended up googling around to learn about gradient descent and of course saw the solution to the first exercise on Github, in blogs, etc, so I do not consider myself to have adhered to the honor code and find the $80 wasted (can't get refund as purchased via iTunes). The mentors are trying hard to be helpful, but it's an old course and they seem perhaps so used to the material that they don't always realise how confusing the exercise instructions can be to newbies.

von Agata L

27. Mai 2018

I have only done week 1 and 2 so far, but I am finding the course hard and frustrating. There's a few errors in the lecture notes, which make understanding hard at times, but once you figure it out it's fine. However, the lectures are nothing compared to the exercises. I have found the first coding exercise extremely difficult and frustrating. With no prior examples, really, how to solve the problems (quizzes are dump in comparison), I struggled all day to solve the normalisation problem, tried two different methods which I think gave me correct results, but the code did not pass. Then my multivariate regression didn't work, theta just wouldn't calculate an I have no idea why, there's nowhere to ask a question as there is no forum. There's no real help on how to solve problems once you get stuck, and I find explanatory notes a lot harder than the lectures. Examples in the lectures are trivial, and then the exercise is like rocket science. I am very frustrated right now.

von Eric S

6. Juni 2018

This course needs to be severely updated and fixed. It is mostly kept alive by the amazing community of mentors, in particular, Tom Mosher. Without Tom, I would have gotten extremely frustrated with the weird quirks that come about during assignments. One important piece of advice: if you can do assignments in an Octave environment such as GNU Octave 4.0.3, I'd strongly recommend it (Althought it tends to crash ofter, so save, save, save!!!).

von Erfan M

27. Dez. 2021

It's a really good course for learning the theory of Machine learning but it's too old for learning how to use it in work. Nowadays there are more powerful programs and programming languages than Octave and Matlab for ML, and this is the weak point of this Course.

von Vyacheslav G

23. Feb. 2019

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

von Elias M

13. März 2022

The instructor is one of the best researchers in the world in machine learning, and the course has surely many things to offer.

One very good thing is that it does not go deeply in linear algebra, so you do not have to know a lot to understand concepts off machine learning.

But the course and videos have many drawbacks. The video sound is terrible, there are a lot of points where the sound is really inaudible. In fact even the transcript mentions [INAUDIBLE] lot of times. The transcript and captions are really bad. There are a lot of mistakes, especially wrong words that completely alter the meaning of the text. The videos have a lot of mistakes. There are even mistakes in the course notes. There is a huge database of Errata that sadly have not been corrected. It is very tiring and frustrating to go through all these Errata in every week course. Lastly, there is a small introduction to Matlab, but the programming exercises require deeper knowledge and more complex commands that you have to find out in your own. There should be larger coverage of Matlab.

To sum up, it is a course that has to offer a lot of useful knowledge, but the videos are so poorly made that make their watching tiring and frustrating, and the instructor does not seem to be bothered to correct them.

von Malcomb M

21. Juli 2017

Content was OK, but quality of teaching was fair at best -- important points glossed over, many not made clear at all, some simply omitted: Bayes classifiers, decision trees, etc, etc.. Audio visual quality of lectures poor. Ng's onscreen scrawls and voice recording were terrible, and there were many mistakes in graphics. Numerous typographical errors in exercise instruction .pdf's. Exercise text itself (ex__.m files) had numerous "pauses" that failed to instruct the user what he had to do (or not do) next, so you had to carefully examine what followed. If more care was put into exercise construction, the "pause" text in the command window would not just say "Enter to continue" but say what coding action was needed to continue. Obviously a lot of work has already been done on interactivity: Quizzes, online Submit scripts, which for me all worked extremely well. But clearly the course could use a lot of improvement in many aspects. Thus I grade it: C-

von Junoš L

21. Feb. 2018

Assuming very little mathematical background, it started very basic. This assumption began to seriously interfere with my understanding when the topics at hand started to become more complex (weeks 3 and 4). Going over the basics of linear algebra again and again (such as matrix-vector multiplication and its notation) distracted me from learning the actual algorithms presented. Dumbed-down hand-written illustrations in place of more rigorous (but in fact simpler!) mathematical expressions also made it difficult to discern the gist.

Otherwise, the course is very well planned. The programming assigments definitely help with understanding a lot and forced me to think about the material more actively.

von Matthew C

31. Mai 2019

Dr. Yang does an excellent job explaining concepts and showing the detailed mechanics of any example he brings up. This being said, I felt the course offered more of an overview, and for anyone with a college statistics and programming course, this won't be very useful, frankly. The course didn't provide lots of new information, and I think much of the actual theory and implementation for ML and its applications would be better broken up into a series of more rigorous courses. This would however, be a good fit for someone working in management who needs a quick understanding of the most basic principles of ML.

von Vahid J

17. Mai 2020

-This was recorded a long time ago, may benefit from an updated; a lot of noise when Andrew is typing, very distracting; videos are not properly modified; many questions in quizzes are very confusing and sometime very opinionated (i.e., hard to figure out what is the correct answer based on the given information); early on the course material was great but its quality significantly dropped towards the end (unclear explanation, inconsistent notation, confusing explanation, a lot of "well ... let assume this the X function, we then call it this way").

von Arvind G

13. Aug. 2020

I was able to finish the course material without a solid grasp of the basics. The assignments did not reflect conceptual understanding. For example, I don't have a precise idea of how backpropagation works even though I implemented it in code. I just had to look up the pseudocode and translate it. I also did not understand SVMs and recommender systems with precision (how they compare and contrast with other algorithms). I think it will be better if larger portion of the code is expected to be written by the student.

von Ranjit B

24. Dez. 2020

While the contents are good and the teaching pace is just right, I am deeply disappointed by the lethargy of Coursera in not fixing trivial errors in its assessment tests. Answers for even some trivial questions are graded as incorrect. Those result in incorrect grading and a frustration. When I am paying to get the assessments and a completion certificate, this is just NOT acceptable!

von Deleted A

13. Juni 2020

Sound clarity is so poor sometime the volume is very low and some point it too hight, how can we concentrate on the course. Online course are stand on two main pillar video and audio, video s good but audio 2/5.

von Anton

11. Mai 2018

Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.

von Timothy B

18. Juli 2020

Out of date, and video quality bad enough to be distracting

von Loftur e

17. Sep. 2018

Assignments are very messy.

von D M

14. Juli 2021

Has a lot of content, but just like you would experience in a university, the delivery comes in the form of:

1. Instructor talks at you for many hours

2. Now go take a test and see how much of what the instructor said stuck.

The course does very little to encourage understanding and comprehension of the material, so if you actually want to walk away with the ability to apply the material that has been presented, you are going to have to look for resources outside this course to complete your understanding.

A​lso, the homework problems frequently feel like they are from an entirely different course. Referring back to the videos for help offers little to no help in understanding what is desired in the homework.

von Richard L

5. Okt. 2021

​I have tried three times to purchase this course unsuccessfully. My credit card is valid and It works for other purchases. Coursera customer support is totally unhelpful. Coursera should treat its paying customers better. I am a subscriber to Coursera Plus and before that I paid for a number of courses. Up to this point, I had been reasonably pleased with Coursera.

von Sabahat

13. Feb. 2021

In the beginning there are notes to explain each video. In the last few videos, there are no notes and it becomes impossible to keep pace with what the instructor is saying as the slides also don't mention the key points that one is interrogated upon in the quiz. The assignments are also extremely tedious and I at least did not learn much from them.