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

119,101 Bewertungen
29,245 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....



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.


Jun 15, 2016

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

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Sep 24, 2019

PROS: Great videos and notes; very helpful quizzes that encouraged paying attention to each video. End of chapter quizzes and programming assignments were sufficiently challenging and rewarding. Mentors were awesome; very helpful and accessible (this is HUGE).

CONS: Would have learned more by being required to create more of the "supporting" Octave scripts necessary for completing each programming assignment, or at least parts of each script, in order to ensure more comprehensive knowledge with Octave/MATLAB (especially with plotting, customizing the inputs so we can test more personal datasets/examples, etc.).

von Anwar H

Sep 25, 2019

The course is really amazing. The explanation is really clear and easy to understand. The experience is really fun and I'm feeling grateful for this. Thank you.

von R. S

Sep 25, 2019

Excellent Stuff from Professor Andrew Ng.

von Aditya K W

Sep 23, 2019

The best knowledge provided on the biggest platform

von Jean-Baptiste V

Sep 23, 2019

Excellent online courses and exams ! Made simple by a Good teacher (Simple is better but Nature is complex)

von LorcanE

Sep 23, 2019

Loved the interactive nature of the course. It was laid out very well. I very much enjoyed the lectures as they were clear and concise.

von ahmad j

Sep 24, 2019

By learning this course, I became more familiar with machine learing that can be implemented in my work. Thank you Andrew.

von Sk. R A I

Sep 24, 2019

This is the first course I started and completed successfully after a five year gap of study. I never got bored the way Prof. Ng taught. There is a perfect balance between theories and implementation throughout the course. I wish that someday I could thank him in person.

von Daniel R M

Sep 25, 2019

I learned an immense amount about machine learning in a relatively small time. This is a fantastic course.

Some caveats are that the technology used for the assignments (Octave/Matlab) may not be particularly useful to you in the future. That being said, it is easy enough to learn that it shouldn't get in the way. This is a course about fundamentals, and is also partially a survey of machine learning techniques.


Sep 23, 2019

Amazing journey so far! This course has motivated me to keep learning machine learning. I wouldn't say the course is tough. All one needs is some inspiration and motivation. I would really like to thank Andrew Ng and his team here in coursera for all of this. Thanks!

von João V A F

Sep 23, 2019

This is a great course, probably the best one to start learning ML in general

von Veeravalli A K

Sep 23, 2019

More examples required.

von Sounak M

Sep 23, 2019

Nice Course!!!

von Carolinne O R M

Sep 24, 2019

Probably the best content on machine learning that I've ever had contact with. Andrew is a legend! Very didactic and profound explanations.


Sep 23, 2019

This course is very useful. Those who want to learn machine learning from basics can join the course. Every topic being discussed in detail. A good place to learn ML

von Kyle P T

Sep 23, 2019

Good overview of the mathematical concepts behind different models and offers good advice for applying different models and what to switch up in certain situations.

von Alfredo F

Sep 23, 2019











von Dinesh K

Sep 24, 2019

Excellent course for beginners. All concepts / algorithms have been taught by instructor in very simple way.

von Jiawei L

Sep 25, 2019

Very good course with detailed explanation.

von 1140325971

Sep 23, 2019


von Alexandre D

Sep 23, 2019

Fantastic class! I highly recommend doing all of the homeworks in python using this repo - You can submit your homework and get grades this way!

von 关欣

Sep 24, 2019


von 毛昌启

Sep 24, 2019

very good

von ajay

Sep 24, 2019

the best teacher ever!. Andrew ng has made the hard to learn complicated topics of machine learning crystal clear.

von Kobayashi M

Sep 24, 2019

I am grateful for the opportunity to take a very wonderful lecture. Thank you very much.