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

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125,428 Bewertungen
30,720 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

SK

Oct 26, 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

RS

Aug 13, 2019

Andrew Ng is a great teacher.\n\nHe inspired me to begin this new chapter in my life. I couldn't have done it without you\n\nand also He made me a better and more thoughtful person.\n\nThank You! Sir.

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26 - 50 von 10,000 Bewertungen für Maschinelles Lernen

von Hou Z

May 05, 2019

Very good instruction for machine learning, and also very very good for new comers!!!

von Nikhil J

May 18, 2019

It was a great learning experience. All the lectures were in details.

von Aditya K

May 18, 2019

It was a very helpful course.

von Prabhu N

May 28, 2019

Course content was awesome, gave me lot of insights. If assignments were in Python, it would have helped a lot to improve my skills. Anyways I would recommend this course to a beginner who wants to understand the logic behind the machine learning process. Thank You AndrewNg Sir!!!

von Rishav K

Aug 20, 2019

It is the best online course for any person wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful.

von Abdul Q

Mar 03, 2018

An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.

von Kothala M K

May 18, 2019

Good Course

von Herman v d V

Jan 15, 2019

My first open online course from Stanford University gave me a lot of energy. As my student years are far behind me (I am 76 years old) it was a discovery to become enthusiast in this new area. And building on my career in ICT, this is a surprising extension on the way systems can help us to develop a better life. Professor Ng is very good in offering in a controlled way many insights in the machine learning - now it is time for me to apply my new knowledge!

von Brian L

May 25, 2019

There's one saying in Chinese that says "一日為師,終身為師" which means once being someone's teacher, even just one day, you're the teacher for the rest of his life. Thank you for all your efforts and I really appreciate it. I'll keep working on Machine Learning and hopefully one day I can do the same contribution to the human society as you did.

von vinod

May 18, 2019

Explanation was very good and assignment helps us to understand the real picture. The way course is planned along with octave exercise, Graphs and visualization of data (X,Y) is very good. Very good course who is starting the Machine learning from scratch.

von Fernando A H G

Jul 21, 2019

Exceptionally complete and outstanding summary of main learning algorithms used currently and globally in software industry. Professor with great charisma as well as patient and clear in his teaching.

von Quoc-Viet P

Jun 25, 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.

von Maksym M

Aug 22, 2018

So much like it. It gave me starting push in this interesting topic. And one important thing that after this course I figured out I need to continue dive into machine learning.

von Akyuu F

May 08, 2019

Excellent Machine Learning Lessons which need little advanced knowledge of mathematics.

von Spencer R H

Feb 03, 2019

It would be nice if it's taught in either python or R. So I do need to take extra effort to learn octave.

von Harshal M

Mar 25, 2019

If this course was in python or R it would have been easier to understand. Octave/MATLAB is not that widely used.

von Sergey K

Jan 24, 2016

Level of difficulty of lectures is not correspond with level of quizzes. In lectures they are talking about simple stuff and then in quizzes they ask you about details they didn't mentioned. You could deducts this information though. But this is exactly the main problem with this course - for quizzes you should deduct and learn by yourself so much stuff, that videos start to be not worth your time.

von Rune F

Dec 18, 2016

Fairly good videos explaining the material, probably worth 4 starts. However, the written support material should be improved. IMHO the video should supplement the written material, i.e. it should be possible to learn the material only by reading. This is not the case, so frequent pausing of videos and making lots of notes is needed if one wants to commit this course to long-term memory.

von Mathew L

Sep 25, 2015

This course is absolute garbage. You get no feedback on your quizzes or assignments and the professor is one of the most boring I've ever seen. It's absurdly frustrating to repeatedly fail without any feedback as to why you're failing.

The lectures are clearly from a math perspective, as the prof simply draws what he's talking about on the slides. His hand writing is poor, and he does a lackluster job of explaining what exactly he's doing.

Finally, pure lecture with no notes is almost impossible to learn, as there's nothing to read and study.

I'd rate this course a 1/10, take the course on iTunes from Caltech instead.

von Rui C

Dec 12, 2015

However good the material and lectures may be, the use of an outdated version of Octave (which is not Mac-friendly and exceedingly brittle, to the extent where the supplied code requires manual patching in Windows and Linux) is a complete turn-off and makes it nearly impossible to complete the assignments on time unless you're prepared to spend at least twice as much time debugging your setup as doing the actual assignments.

I'll come back when this is done with R or Python.

von Eric J

Mar 27, 2018

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

von Marius N

Oct 31, 2017

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

von Karl M

Aug 11, 2017

Very nicely explained the mathematical topics, even for people like me with some phobia regarding large formulas. Useful hands-on experience with MATLAB coding, which I would have had to learn anyway.

von Anup B D

Apr 22, 2017

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

von Natasha

Oct 15, 2016

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.