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Kursteilnehmer-Bewertung und -Feedback für Maschinelles Lernen von Stanford University

4.9
111,535 Bewertungen
27,474 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

AA

Nov 11, 2017

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

DW

Feb 20, 2016

Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.

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25826 - 25850 von 26,598 Bewertungen für Maschinelles Lernen

von Ramana V

Sep 05, 2017

There are many new examples using ML recently, 30 minutes session on the latest examples by 2017 should help us and excite us more. Also how ML can be used in other technologies like Data Science and others also could help us in building our career in right direction

von Subhasarthak S

Sep 08, 2017

This course is a great course. I appreciate the efforts of professor and other teaching assistants to make things easier to understand. It will be great if Professor explains how to create data sets in real life (probably in some additional videos). Thank You for such a great course.

von Hang Y

Sep 10, 2017

Pretty well, but it seems that i'm not used to learning by video

von Ilker T

Sep 21, 2017

More examples are necessary.

But it was a very good course.

Thank you.

von Киреев Е Р

Jun 15, 2018

I wish exercises could be better.

von zeeshan s

Sep 05, 2017

Great course

Teaches the basics of machine learning

However, outdated since it uses octave and matlab while now people use tensorflow (mostly).

But the concepts learnt could be applied to machine learning in general.

von Gokul G

Jan 16, 2018

This course is a very well structured for someone to give complete understanding of how machine learning algorithms are designed and executed

von davidfeng

Apr 02, 2018

The only math part is probably some simple matrix and vector manipulation. The course is designed to be simple but a little verbose. If this course is more compressed, i'd give 5 star. Content wise it is worth to go through. Also, this course did not touch security aspect (adversarial machine learning not included in this version of the course) By the way, i am not a data scientist but do work in cyber security field.

von andrew f

Feb 13, 2018

Video lectures are diverse, thorough, wide ranging, and Ng is a good teacher. The programming exercises were difficult and some of the linear algebra and matrix manipultion was obscure, especially regarding matrix transposition and matrix multiplication ordering. These sometimes did not line up with videos, which proved frustrating.

Overall very good though.

von Yannis S

Oct 18, 2017

instructor used simple theoretical examples to explain difficult ideas, while using realistic problems revealing how these methods can be applied in practical cases. Quizzes were important for keeping interest high during the lectures while programming was fun and rewarding. The submit system worked very well. Overall, congratulations for the nice presentation, the good organization of the lectures and that of programming problems and their evaluation. I should also say: it was inspiring.

von Harshal K

Mar 15, 2018

It is very helpful for a person who wants a proper explanation on machine learning because this covers all the important algorithms from scratch. the tutor also explains everything clearly with no ambiguity in the audio. All in all this course will take the learner to the field of data science by introducing many concepts.

von Ambuj B

Feb 25, 2018

Just completed my first week of the course. The course is interactive and starts from the basics to go to the more complex problems, which makes it easy to understand.

Removing one point because there is sibilance in the audio and sometimes audio is hard to understand.

von 朱祖伟

May 31, 2018

Will be better if new version with python is ready.

von Krishna P P

Jun 28, 2018

Organized curriculum, Prof.Andrew places high emphasis on intuitive understanding of the subject rather than drilling equations - My only wish is, they had taught it using Python which is far more versatile.

von Macklin F

Nov 24, 2017

Great course. The programming assignments at times were a little too guided. I would have liked a more open project to work on, but learned a lot.

von Evan L

Jun 06, 2018

Details are useful and it can help you understand the related topics.

von Irtaza A

Mar 31, 2018

An excellent course, although for backprop exercise the main material was not enough and inefficient and confusing according to mentors.

von Roghayeh M

Apr 19, 2018

It is great, I recommend it.

von Juergen B

Dec 09, 2017

Extremely helpful course. Especially that it shows ways to test and tune the own work and to find out, what to put effort on. I missed some more on clustering, eg how to measure the quality of a clustering. I know that there're some KPI's but after the class I still cannot interpret them. So one less star and I hope that you add this little thing to the course.

von Veltzer D

Sep 25, 2015

Excellent course, could be helped by having some prerequisites and then dispensing with the parts that breeze very secularly through weighty topics such as Linear Algebra and general interpreter based programming.

<3

von johnny l

Mar 03, 2018

teach me a lot

von MANI K K

Feb 05, 2016

very nice

von Cristian G

Mar 22, 2018

Excelent course, but last weeks don't have practical excercises.

von Daniel P

Mar 05, 2018

Really informational and interactive. Good job andrew!

von John D

Apr 08, 2018

Excellent in all, but one, aspect. Great lectures, course notes, and programming assignments. My only complaint is the level of "passing" 80% for the quizzes. Most quizzes involved many "Check all that apply" and since there were no part marks, achieving 80% actually meant closer to 94% with the frequent multiple correct/incorrect answer options.