Chevron Left
Zurück zu Maschinelles Lernen

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

4.9
121,521 Bewertungen
29,841 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

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)

Filtern nach:

25701 - 25725 von 28,946 Bewertungen für Maschinelles Lernen

von kushalv5678@gmail.com

Jun 13, 2018

Great Course with good pointers on evaluation of results under different circumstances

von Malcolm C

Oct 10, 2016

The course content was systematically organized with increasing complex algorithms covered as the weeks progressed. Prof Ng's enthusiasm for the subject also made the course enjoyable. Thank you so much.

von Brandon H

Apr 19, 2017

I finished this course July of 2016, not sure why I am just commenting now. In any case, for those looking to get into the field of machine learning/data science, or those who are in a quantitative discipline looking to bolster their skill set, this course is invaluable. Dr. Ng is incredibly thorough with each concept and provides numerous examples to illustrate his points. His lecture notes may be a little hard to follow if you don't meet certain prerequisites (calculus, linear algebra, etc) but they're well worth the read regardless.

von imtiaz

Apr 16, 2016

Great Course, Thank you Professor :)

von Rohan S

Jun 06, 2018

This course has been an amazing experience and a good starting point

von Ramez A

Jun 08, 2018

Wonderful ! Reading more resources on ML does not feel like reading about black magic anymore -:)

von Kareem I A

Nov 08, 2019

I really enjoyed this course and learned alot about machine learning. It is so rich and talks about alot of the practical problems that I would face in my career. Thanks you.

von Fredrik N

Aug 31, 2017

Excellent teacher

von siddamshetty s k

Apr 02, 2019

i have learnt completely it is very usefull

von 은진 최

Jul 01, 2018

This lecture was really helpful to learn Machine Learning for beginner! I've already recommended to my friends who are interested in ML, and I definitely recommend you who are considering taking this course! The videos are really well described to understand the concepts, and it was not really boring to take this course, because of exercises which are given for almost every week! Each exercise is really exciting to experiece practical problems like hand-written digit recognition, spam mail classifier, movie recommender systems and so on. Finally, I want to say thank you to the instructor, Andrew Ng for providing this great course! I learned really many things from this courses :D

von Yanjun A

Dec 29, 2017

Maybe a bit outdated but still very good to start with if you want to get on hand with ML.

von Ashutosh T

Sep 15, 2018

Very good course for Machine Learning. Loved every video, quiz and Programming Excercise. Thank you so much for such a good designed course.

von Andrew M

Sep 04, 2017

Very good course.

von Jun M

May 26, 2019

This class is one of the best class I have ever taken.

If you are interested in machine learning, I recommend taking it.

von Rami M

Jun 07, 2016

Amazing!

von Neha M

Mar 24, 2017

Great course

von Andrey P

Jan 05, 2016

Good stuff

von Bright U

Mar 22, 2019

Great Teacher and Great Learning!

von Jean M

Nov 24, 2017

This course was great, very well explained. Everything is motivated so we always feel that we're learning useful things. Maybe one problem is that the exercices tend to hide how much boring work it takes to put up a program with the correct data, import it and eveything (since we only have to complete the core). But that goes along with the fact that exercices are a lot of fun.

von Soumit B

Apr 28, 2017

Awesome course for starting Machine learning.

von 枫霜凌

Oct 08, 2017

讲解得很详细

von Tanisha L

Feb 02, 2019

very nice. all concepts are well explained.

von Anh Q D

Jun 29, 2017

Excellent course in Machine Learning. Wonderful lecturer indeed!

von Sonam P

Jun 23, 2017

Great place to start learning ML.

von Yulong W

Mar 12, 2016

一边学一边练,课程很好,100个赞!