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

4.9
116,844 Bewertungen
28,665 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

HS

Mar 03, 2018

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.

CC

Jun 20, 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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27276 - 27300 von 27,815 Bewertungen für Maschinelles Lernen

von liuyufei

Sep 05, 2018

高开低走吧,前面章节确实比较好,深入浅出;即使是中学生,也完全能看懂。但是到了后面趋于流水,实在是太浅了,深度严重不足。编程练习也太简单,基本上就是按照前三周的模板来改造。

von Kumar G

Sep 07, 2018

The handouts can be made better.

von Ignacio Z P

Sep 07, 2018

Maybe should be better using python instead of Matlab.

von Mengyu

Sep 06, 2018

Good course. However, time is not very well arranged. Some weeks have too little content.

von Stephen G

Aug 24, 2018

A lot of care is taken to explain concepts in detail, also for people without much of a maths background. The programming exercises are very well constructed, although they are not very challenging for regular programmers.

von Khalid Q

Aug 08, 2018

Really good introduction to machine learning.

von Kevin J

Aug 08, 2018

Great introduction to Machine Learning. I really enjoyed this course and learned a ton!

von Zeeshan S

Aug 07, 2018

Course has become a bit outdated.

von Kalin D D

Jul 23, 2018

Good introductory course. Need more exercises and tests with more than 5 questions

von Jasvith B

Jul 17, 2018

There was no exercise to build from scratch and no use of other languages like Python with tensorflow type

von David E

Jul 27, 2018

Course videos are fantastic. Course materials are VERY poorly organized. Seemingly no centralized location for zip files containing data, code, etc? In fact, I can only find subsets of the materials scattered throughout discussion forums. Tutorial materials are nowhere to be found at all. It's making me a little bit loony.

von Nicholas G

Jul 28, 2018

While I didn't find anything particularly difficult, I found the nomenclature used for variables and features was a bit of a strain. Allthesame, the coverage of regressions and unsupervised algorithms was excellent and (after accounting for the labeling methodology) easily digestible. Dr. Ng's exposition made those particular subjects enjoyable to explore and provided enough detail to enable me to understand the maths and algorithms in a language agnostic way.

von Romain X

Jul 25, 2018

Grate course ! It would have been nice to talk about convolutional networks as well.

von Harshita c

Jul 30, 2018

Really good course just dissapointed as the grade is not mentioned on the certificate

von jay t

May 02, 2019

Great teaching!

von Aditya S

May 03, 2019

This is the best course for beginners. It teaches all the topics of machine learning with best quality quizzes. The only reason I am giving four stars is because it uses octave language instead of python.

von Saiavinash B

May 01, 2019

can be improved in a detailed fashion

von Sridhar C

May 01, 2019

Good introductory course.

von Kelvin Y

Apr 17, 2019

Pretty good. Definitely not an expert yet, but it was very useful.

von Ayaz K

Apr 17, 2019

Its an amazing course for beginners, but it would much better to be in python rather than octave.It helped me a lot in understanding the concepts of Machine Learning.

von Alberto R A

May 04, 2019

very good class, but a little bit outdated.

von Rajpal s D

Apr 19, 2019

This course is really good and a step towards the real world of machine learning and it's applications,

Thanks to coursera for such course.

von Amar D

Apr 19, 2019

Informative course

von Aditya J

May 06, 2019

Great Course