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Bewertung und Feedback des Lernenden für Maschinelles Lernen von Stanford University

169,980 Bewertungen
43,472 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....



25. Okt. 2017

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts


24. Juni 2018

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.

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301 - 325 von 10,000 Bewertungen für Maschinelles Lernen

von Hamed B

5. Juni 2019


von Bhanu p T

1. Jan. 2019

Loved it. Easy and Excellent Course

von Lưu V L

5. Aug. 2020

best ML course in the world !!

von Jaspinder S V

8. Aug. 2015

Awesome course for beginners.

von Mulat Y

14. Feb. 2020

Machine Learning

Data Science

von Mewada A J

5. Aug. 2020

best experience of learning

von 梁驰

8. Feb. 2020


von chandan k

6. Juni 2019

Great course to study!

von Eugene M

4. Jan. 2019

Very useful course!

von Joy F Y

7. Aug. 2015

It's very useful

von WANG B

8. Aug. 2021

Andrew Ng yyds!

von Pavel K

6. Juni 2019

A great course.

von Hacker O

17. Juni 2019

very good!!

von Stephen M

5. Juni 2019

Very useful

von ylfgd

6. Juni 2019

very good

von Thierry L

4. Jan. 2019


von Saiful A

7. Aug. 2015

Very Nice

von Vivek K

13. Dez. 2018


von Nazir A Z

29. Juli 2021


von Lichen N

28. Aug. 2019


von Sam C

2. Jan. 2020

I'm not crazy about online learning. There are certain aspects of classroom learning that online learning can't give. But as far as online learning goes, this course is probably about as good as it ever gets.

Prof. Ng gives very clear expositions of the fundamentals of machine learning. Anyone taking this class and completing the assignments will be ready to apply machine learning to at least some simpler real world problems and should be in a position to quickly pick up more advanced techniques for more complex problems.

The exams are fair (although I think some more work could have been done to make many of the questions less ambiguous). The programming assignments can be a time sink, but I don't think they could have been any shorter and still give valuable practice in using the techniques outlined in the lectures.

Students who already have a background in linear algebra or the basics of data analysis might find the pace of the class in the early units, where Prof. Ng deals with linear regression, to be rather slow. But if you can get through those early units, you will definitely find yourself dealing with new material (and occasionally appreciating the initial slow pace).

Octave/Matlab is the only language in which the assignments are accepted. I personally would have voted for python. But Prof. Ng spends a few lectures telling you all you need to know about Octave/Matlab, for the purposes of the course. (To save time, I would advise that you spend a day or two learning the language on your own before starting this course. That will allow you to stay that much more ahead of the due dates. But maybe that's just me.)

One word of warning is that, as a friend of mine said after taking a machine learning class in a traditional university classroom, this material makes machine learning accessible, but also takes the "magic" out of it. If you are impressed at how Netflix can be so good at recommending new movies for you to watch, well, after taking this class, you won't be impressed anymore. You'll probably be figuring that, yeah, they probably have some tricks I don't know about, but I could do 90% of what they're doing myself! Which actually means it's a good class!

One thing I definitely would have added are some words at the end of the course about what the "hot topics" are in machine learning, and suggestions about where to go from here, what topics would reward further study, and what books, websites etc. are available for studying them. For example, some words on where to study how and when machine learning turns into full blown artificial intelligence would be appreciated.

The only real gripe I have is that the assignment due dates really didn't give appropriate regard to how busy real life can get during the winter holidays. After all, the big selling point of online learning is flexibility! Right?

In summary: I figure this class is about as good as online learning will get. The instructor is very clear; the assignments are fair and useful. I would have done a few things differently, but nothing is ever perfect. This is a good class for anyone wanting to know the basics of machine learning. Four stars.

von Saideep G

9. Apr. 2019

Very well made, well paced. Better than majority of college courses. Some errors do pop up midway through the course that should be addressed. It can be frustrating to push through these issues sometimes but they are the only thing keeping from 5 stars.

von Cheung C H

8. Dez. 2021

1. better teach in Python

2. sound recording quality can be better

3. Overall content is good

4. please provide more math details, such as in back propagation, and partial derivative is actually very basic where every undergraduate should already know

von Doreen B

9. Juni 2019

Well explained, at the end of this course you will understand the subject and hold coherent conversations about it. Matlab implementation relatively simple, maybe too much so. Highly recommended course.

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.