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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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25876 - 25900 von 27,800 Bewertungen für Maschinelles Lernen

von Tripti B

Feb 21, 2019

Classes are nice. I would have appreciated little more mathematical proof of algorithms as optional videos for those who are interested.

von CALIMANUT I C

Feb 28, 2019

Octave is obsolete for machine learning. Try python/R

von Jason C

Feb 28, 2019

Content-wise excellent and well-paced. However often lecturing made too many repetition that seems redundant. Overall a great introductory course.

von Senthil K

Mar 01, 2019

Awesome material , Got a big picture of machine learning. Would have gave 5 star if Python or R is used instead of Octave.

von Himanshu K

Mar 02, 2019

Very insightful and detailed.

von Gridin I

Mar 03, 2019

Good course! But the mathematical calculus is being presented in some unstructured way.

von Berkant

Feb 21, 2019

I found the course quite enjoyable. Thank you for the work and effort. As a suggestion: what I think is missing (or perhaps not clear for me) are directions to read and learn the mathematical details of all the algorithms that were discussed. I can probably do it on my own but if there were pointers I would have been much happier. As is at some points I was considering dropping the course. It was the programming assignments that kept me going on.

von Dheeresh A

Mar 05, 2019

Did not teach tensorflow

von A N K

Mar 07, 2019

Good course for beginners

von Ravindra B K

Mar 10, 2019

Excellent course. But one suggestion : replace octave with Phtyon. Adding python in place of octave will improve the learning experience.

von Krishna R

Mar 10, 2019

The reason I have given only 4 stars not five stars because there is much more well not more but few thing which can be included in this long course. Finally I can say that this is the one I'm looking for to build a Machine Learning from scratch and I Mainly thank you Andrew NG sir for giving this wonderfull classes for the student like me who are intrested in the Artificial Intelligence.

von Shushrut G

Mar 08, 2019

Even though I'm not very comfortable with programming with Octave , but this course is great !!

Prof Andrew teaches each concept in detail to make sure that the concept is understood clearly by students. I shall highly recommend this course to any one why is starting in machine learning or even in programming. A rough knowledge in stats and probability is recommended before taking this course.

von Imad E A

Mar 11, 2019

One problem: the sound quality is bad!

von Esmail K

Mar 13, 2019

It's very useful

von Vincent V

Mar 17, 2019

Great course that gives you the details of what machine learning actually does behind the screens. It is a very nice feature that you have to take small tests (quizes) and programming assignments to check whether you understood the week's topic. One thing that was less in my opinion is that the exercises were prepped a bit too much. But if you are interested enough in the whole picture, you can check out the prepped exercises as all code is provided to you. Once again, superb course!

von Sebastian D

Mar 17, 2019

The course provides a great overview on foundational concepts and algorithms for machine learning. Material is presented in a very engaging way by Professor Ng and programming exercises help to deepen the student's understanding.

My complaints with regard to this course are minor:

1) I had some trouble really developing an intuition for back propagation in neural networks based on the course material (a look at free Google ML material helped, though) and I am still not 100% sure if I understood SVMs

2) The course exists for a while now and it would be nice to see it updated to the current state of the art in ML

3) One or two more weeks of course material covering other concepts such as decision tree and random forests, and maybe a brief glace at Bayes-based approaches would have been nice

I rate this 4 out of 5, but if possible, would also give it a 4.5. Also despite the potential improvements just mentioned, I can recommend still recommend this course to anyone who wants to get a better grasp at machine learning.

von Jingjing G

Mar 19, 2019

A little bit too easy.

von Tamkin R

Mar 18, 2019

This course is amazing in itself but this platform makes it more interesting.

von Neel S

Mar 19, 2019

A great course for anyone who needs to learn the inner workings and the essence of machine learning. What is the math involved and how the machine actually learns through it. Also, the hands-on experience via the programming exercises and quizzes are quite amazing as well.

von XuBo

Dec 27, 2018

很详细,缺乏深度。

Very detailed videos, lack depth.

von Martijn R

Dec 27, 2018

Excellent course, the instructions and information are clear and concise. The course gives a good overview of machine learning concepts and applications. However, the breadth is also a downside, since several concepts are only touched upon and not dived into more deeply. This is a shortcoming for more academic folks.

von Eason L

Dec 27, 2018

Nice and awesome.

von koppakasaivasishta

Dec 28, 2018

I am glad to say that i am comfortable through out the course.The way sir delivered the lecture was good enough to be understandable by a beginner.