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

4.9
120,940 Bewertungen
29,696 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

CS

Jul 16, 2019

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).\n\nAndrew Ng is excellent

QP

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.

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151 - 175 von 28,818 Bewertungen für Maschinelles Lernen

von Carlos E R d S

Jul 16, 2019

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).

Andrew Ng is excellent

von 王奇

Aug 07, 2015

吴恩达老师的这门课帮助无数学生了解了什么是机器学习,虽然有时候作业会很没有头绪但是通过努力研究一般都能做出来,而且满满的成就感。。感谢andrew

von Luca W

Jan 19, 2017

Thank you Professor Ng for taking the time to produce such a phenomenal course. As mystifying as machine learning can appear to be, your well-paced and digestible teaching style gave me the opportunity to understand. With fantastic lectures, mid-video quizzes, end of topic quizzes, and programming assignments, you as a student are given all the resources you need to absorb the material.

These eleven weeks really gave me the perspective and knowledge I sought for. This is the first online course that I have taken and I am inspired and excited for the future of machine learning and e-learning. The final heartfelt video was a perfect conclusion and I wish to return the sentiment of gratitude and appreciation.

Thank you again, and rest assured that your teaching is having a profound impact on peoples lives across the world.

von Jerome T

Mar 06, 2019

I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.

von Nils W

Mar 23, 2019

Great course, but the sound quality is quite bad.

von Mehdi E F

Mar 19, 2019

Very instructive course.

Thank you.

It would have been great to get an OCR exercice at the end.

von MAHESH Y

Apr 09, 2019

it is one of the best course for beginners in machine learning, the only thing it lacks is its python implementation. If there is the python implementation of this course then no other course is better than this one

von Saideep G

Apr 09, 2019

Very well made, well paced. Better than majority of college courses. Some errors do pop up midway through the course that should be addressed. It can be frustrating to push through these issues sometimes but they are the only thing keeping from 5 stars.

von Mohd F

Nov 08, 2018

There is a lot to say about you Andrew sir but in few words - "Thank you very much for teaching us the ML concepts in such a beautiful manner "

von Doreen B

Jun 09, 2019

Well explained, at the end of this course you will understand the subject and hold coherent conversations about it. Matlab implementation relatively simple, maybe too much so. Highly recommended course.

von Sai V P

Aug 05, 2019

Better upgrade from matlab to Python

von Shitai Z

Nov 19, 2018

Too easy for people with background in machine learning. But would be a good introductory one if you have zero understanding in machine learning and want to change your career track.

von Eric S

Jun 06, 2018

This course needs to be severely updated and fixed. It is mostly kept alive by the amazing community of mentors, in particular, Tom Mosher. Without Tom, I would have gotten extremely frustrated with the weird quirks that come about during assignments. One important piece of advice: if you can do assignments in an Octave environment such as GNU Octave 4.0.3, I'd strongly recommend it (Althought it tends to crash ofter, so save, save, save!!!).

von Mirko J R

Apr 02, 2019

Excellent lessons by Prof. Andrew Ng.

However very poor support. No answers from any mentor along lessons, you should resolve all doubts by yourself.

I had a problem with my ID verification, I was waiting for a long time without any responses.

Also, it's difficult to contact persons who could support you, I tried to contact someone but just found a Bot. Terrible support.

von Jerome P

Mar 30, 2018

Good introduction course, giving an overview of machine learning algorithms and some methodology. Off course a lot can be added, but it's a good start for people with little to no knowledge or experience in this field. A few points that could be improved: I would like to have better material support for each section. Marked-up slides are not a great support for reviewing the different sections afterwards.

It would not hurt to provide a little bit more theoretical background and justification when covering the different algorithms. Andrew Ng almost apologizes when going into mathematical equations, but this is fundamental to machine learning.

quiz assignments are rather easy. They could be a little more challenging

I would rather have the programming assignment using R or python than Matlab.

But still a decent course overall I think.

von Marcin K

Mar 02, 2018

The course covers a lot of material, but in a kind-of chaotic manner. There is a lot of math, so if you're not familiar with linear algebra you may find it really difficult. Personally, I don't quite understand the approach. The goal of this course seems to be to teach people how the algorithms work, and if so - there is just enough math, for the students to get lost, but not enough of it to truly understand what's going on internally in the algorithms.

Also, the vectorization techniques of the provided formulas is not quite well explained, and it's left to the students to figure it out. This lead me a lot of times to trial and error approach, when I was just trying different approaches until something worked, but it was still hard for me to understand what really happened. Oftentimes I found myself spending more time on trying to understand how the matrices and vectors are being transformed, than actually thinking how the algorithm works and why.

Another thing is that after finishing the course, you have almost ZERO experience with real-world tools you're supposed to use for real-world projects. I'm thinking TensorFlow, R, Spark MLib, Amazon SageMaker, just to name a few.

On the bright side, the course teaches several general good practices like splitting the datasets to training, cv and test. It also explains very well how to work with different ML algorithms, how to monitor they are "learning well", and how to fine-tune their parameters or tweak the inputs, in order to gain better results.

The course ends with assuring students that their skills are "expert-level" and they are ready to do amazing things in Silicon Valley. That is obviously not true for the reasons I already mentioned (e.g. lack of tooling experience). I see this course as a starting point for anyone who seriously wants to go into ML topics, and to actually understand at least some of the internals of the 3rd party libraries he'll end up using. But don't think you'll end this course with any practical knowledge, or that you'll be ready for real-world problem solving.

von Vyacheslav G

Feb 23, 2019

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

von Saleh a h

Jul 14, 2017

good so far

von Ivan Č

Feb 24, 2016

Certificate is expensive!

von Loftur e

Sep 17, 2018

Assignments are very messy.

von Anton

May 11, 2018

Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.

von Samuel

Feb 19, 2018

The course is not for people with not mathematical backgrounds plus its using matlab.. these days R and Python are more used in the industry for ML. I found to this course via friends that said it's hard but very recommended.. i think there are easier courses online that can deliver the same concepts

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von Hu L

Feb 14, 2018

Too easy and too slow

von Manik j

Sep 26, 2019

This course is good but it cannot be able to clear the very basic knowledge of the student and also donot they tell to how to code the things

von Ross K

Oct 10, 2015

The course is more an exercise in flexing Ivy vernacular than it is actually teaching. The learning curve is too steep to be useful to the majority of potential registrants. You're interested in this course either to (a) learn something about an exciting and ever changing field and/or (b) to have the Stanford logo on your LinkedIn profile. In both cases, move on. The curve is far too steep to be useful or to merit the countless additional hours of background learning the course should have done to bridge the gap.