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

4.9
114,367 Bewertungen
28,116 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

AD

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.

MN

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.

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25901 - 25925 von 27,244 Bewertungen für Maschinelles Lernen

von Konstantin K

Nov 06, 2015

I wish programming assignments were a little more in-depth. Otherwise a great course. Exceeded my expectations.

von Vanditha

Jul 09, 2017

Impressive and well explained!

von Kaushal S

Apr 07, 2016

Exceptional content and absolutely amazing tutor.

von Nathan B

Oct 12, 2015

Part of Neural net could be better explained

von 陈然一鎏

Aug 09, 2015

sometimes Chinese subtitle goes wrong, but generally it's a perfect net course.

von Bandar M

Sep 10, 2015

this is a great course to begin your way in machine learning.

von Özge A

Oct 13, 2015

very fine :)

von Kovelenko A

Nov 08, 2016

The course is great, but some materials have low quality. Still it is really good.

von Jayneel P

Apr 07, 2016

Extremely well designed course to introduce many practical aspects of machine learning.

von Jesu R A B

Feb 26, 2018

Really a good course. Will teach how the ML algorithms are working and how to tune parameters of algorithms to get good model

von Sergey S

Jan 28, 2016

This course is good but not the best I've seen

von Tara T S

Jan 16, 2018

This is a very nice online course to gain a basic understanding about machine learning. It's going to build you a very good foundation on this field.

Having basic programming and statistics skills will help you heaps on this course.

von Walter d J

Dec 19, 2016

This is a great introduction to an topic which has a lot of practical application. I found it challenging but rewarding.

von Sahil S

Sep 19, 2017

Nice for starting Machine Learning from scratch.

von Guangxiang Z

Aug 08, 2016

The course is very good,throgh taking this course,I have a general picture about machine learning .But ,there still exsists several problems,for example,the quality of the videos are low,and I am really looking forward to new version of the course.

von Xinpeng H

Jul 21, 2017

Good introductory course. Assignments are too easy.

von Eiji H

Apr 11, 2016

Good. However, I need more detail and advanced exercise.

von ASHOK K K

Sep 02, 2017

voice recording is not so clearly.

von 冯力

Dec 08, 2016

Very good , but lack of mathmatical proof .

von Utsav N

Aug 16, 2017

simple and easy to understand

von Ian C

Dec 23, 2016

A worthwhile ML course. Could be shortened to 8 weeks.

von Vijayram R

Mar 30, 2016

very good

von Ingo S

Jun 27, 2016

Great course, Andrew Ng does a good job at easing you into this complex subject, particularly for somebody like myself without much maths background. Looking at other video courses on the subject, I think more interactive visualisations could've been a great help, for example to gain some intuition about polynomials with different input parameters. Drawing lines on screen works, but I've had many revelations when working with the interactive tools linked from the wiki content. In some course exercises, the tutor and lecturer disagreed on the approach (e.g. vectorised vs. iterative), so the videos/PDFs were inconsistent with discussions/tutorials - a bit confusing.

von Stefan A

Jan 04, 2018

Some stuff is a bit outdated, otherwise great course!

von Eui S A

May 10, 2016

A good introduction to machine learning. Andrew Ng has a more in-depth course on Stanford Engineering Everywhere, if anyone wants to learn more.