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

114,000 Bewertungen
28,029 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....



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.


Jul 14, 2019

This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

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25801 - 25825 von 27,178 Bewertungen für Maschinelles Lernen

von George L

Aug 09, 2017

Andrew Ng does a very good job of stepping students through the various technologies. The programming assignments are challenging without being impossible, and relate to the subject matter at hand. His pace is good in most places, although occasionally it was difficult to following the thread of logic that brought us to various conclusions. All in all it was a good class that was immediately applicable for me in my career.

von Sameer

Sep 23, 2017

Great course, very relevant. Not perfect, but not far from it.

Main difficulties are in notation - i's and k's and j's can get confusing. Has anyone worked out a more clearcut way of differentiating between things?


von Archit J

Jan 24, 2018

This course has been an amazing journey, learning with Andrew was really fun. Love and blessings, Andrew. Thank you:)

von Amit K

Jul 03, 2017

A perfect course to start in this vast field of machine learning starting with the roots of concepts using basic mathematical tools like matlab and octave

von Debdulal D

Nov 27, 2017

Awesome teaching methods, materials and sequence of learning events, but the assignments could have been more flexible if python or R would have been acceptable.

von Mathïs F

Jul 29, 2017

Miss a star for video quality, but the course is great otherwise.

von amrut p

Sep 23, 2017

good course for diving into machine learning. Amazing teacher, good learning resources everything is explained in a very understandable way, complicated concepts were made easy by Prof. Andrew.

application of the algorithms not touched to the level i was expecting but all in all a very good course for people diving into ML.

von C V K R

Aug 06, 2017

The assignment submission part is quite confusing please improve that.

von Toke Z

Nov 05, 2017

A really excellent course for practical implementation of various algorithm. This will suit users who are not seeking a deep mathematical understanding of these topics, even thought Andrew tries to show the general idea for a lot of the concepts. Overall a great course, and good coding practice! :-)

von Aswin S

Aug 09, 2017


von Stephen F

Sep 03, 2017

Quite good overview about different topics in machine learning. Could be a bit more mathematically grounded though.

von Alain S

Nov 04, 2017

great intro even for the one who just want to get an idea of what ML is all about and what are the basics knowledge many current companies are using to build their products

von Paul S

Sep 20, 2017

I learned a lot in this course. I am surprised that so much known errata has been left as is. The forum resources catch most but not all of it. There is room for improvement.

von Gillian B

Sep 17, 2017

Thoroughly enjoyed this course - it was my first Coursera course. I have a degree in Pure Maths and IT, but I graduated back in 1995 so it's over 20 years since I did anything academic. This brought so much back and introduced me to ML which is absolutely fascinating and I intend to study more on ML e.g. try some of the dataset problems on Kaggle. I did this course purely for recreation and got through it in just over a month (I work full-time). I liked that I could work at my own pace and get ahead so that if other commitments took over, I could put it aside for awhile and still be on track. I have knocked off one start because: I would have liked more practice questions/exercises to cement some things (it was easy to forget what some of the variables stood for in some of the longer equations; the audio quality was not great. That said, I realise that this was the first MOOC from Coursera, so I expect that's been improved in other courses since.

von Sky W

Oct 14, 2017

Overall very good, however this youtube video by 3Blue1Brown on Neural Networks made some features about neural networks at lot clearer. Specifically, in understanding what the hidden layers actually do. Including a similar introductory video to that topic would have helped me.

von Milind S

May 27, 2018

The content is very relevant and good. Couple of videos need some better editing. Regardless, the course is very informative and full of practical advice.

von Soham G

Dec 06, 2017

Loved this course.Using MATLAB for matrix calculations made work easier.Programming assignments and quizzes highlighted areas which I needed to work on so I found them helpful.Last but not the least,loved Prof.Ng 's style of teaching and also am grateful to Tom and other mentors for keeping up with questions I and other students had.

von anand t

Mar 22, 2018

awesome course

von Jonas A

Nov 23, 2017

Only 4 out of 5 due to the fact it was too easy to pass the programming exercises without understanding what actually happened. Other than that, great online course. Highly recommended!

von Maurizio C

Apr 04, 2018

I think that use python should result in a better choice than Matlab/Octave for the exercises but the course is great

von Sherwin R

Dec 01, 2017

MATLAB is outdated in my opinion

von Kilian M

Dec 21, 2017

I wish there was an updated version of this course since the field is moving so quickly.

Also, I wish Andrew had had access to a better microphone.

Other than that I very much enjoyed this course and learned a lot. Thanks!

von Daaloul C

Oct 22, 2017

Great for an intuitive and practical introduction to machine learning, however the theory is left behind, which may be bothersome to some (it was to me anyhow). However, Mr. Ng really couldn't be clearer about what he discussed here !

von Raghavendra B R

May 05, 2018

This Course is well structured to provide basic understanding of the most widely used Machine Learning concepts and algorithms, It is the best Introductory course one can rely on to start their Machine Learning journey from.

von Ian B

Jan 04, 2018

Good class, though at times I felt like the quizzes were just notation exercises. The programming exercises were very insightful. I feel without a strong background in linear algebra this class will be extremely difficult.