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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)

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101 - 125 von 28,962 Bewertungen für Maschinelles Lernen

von Chandan K

Jun 06, 2019

Great course to study!

von Harshit S

Jun 06, 2019

Very Good Course to start into machine learning, It uses Matlab which is very useful, all mathematics behind different algorithm nicely explained by instructor, Instructor is very good teacher

von Nathan M

Jun 05, 2019

I thoroughly enjoyed the videos and programming exercises. I think Dr. Ng has great insights that will help me approach future ML problems with greater understanding and efficiency.

von Jorge L R C

Jun 05, 2019

Even being for a "old" course, it has the very best ground of concepts and techniques of Machine Learning. I am very much satisfied and have learned a lot.

von Nimish B

Jun 05, 2019

I loved this course. Helped me learn about concepts specific to Machine Learning in a very interactive and intuitive manner. Working on Octave took time at first but is easy to pick up. Thank you Andrew Ng for this really well thought out curriculum!

von Vaibhav J

Jun 05, 2019

The explanation of each and every topic is so simple and easy. The course is taught by prof. Andrew Ang and covers the major concepts of machine learning. He also provides a good intuition about the topic so to understand them better. Overall this course is awesome and I would highly recommend to someone who is a beginner in Machine Learning. I am very grateful to Professor, Mentors and the Coursera for this amazing journey of 11 weeks in machine learning.

von Tobias T

Jun 05, 2019

I've tried DataCamp and recently take my first course in Coursera. The difference is huge and important if anyone wish to learn more about ML or DS. This course does not focus much on 'just coding' the answer. It aims to teach you the logic, basic maths behind ML algorithms.

The coding exercise is challenging and fun aswell. It doesn't give you any 'fill in the blanks', so basically, after each exercise, you properly have some good understanding about the logic. Using Matlab/Octive is much better than I expect. Not that it is easy to use/understand, but it let you understand the Math better. e.g. when to transpose, how to use look at dimension before writing any codes. These exercises are at a level which you can easily transcend your understanding and knowledge to whatever Python or R you are using. !

von Stephen M

Jun 05, 2019

Very useful

von Tony X

Jun 05, 2019

Quite good, suggest for beginners. There is no much mathematics knowledge deeply involved.

Andrew Ng used a simply way to describe machine learning algorithm. It's really helpful to understand the concept.

Thank you very much!

von Hacker O

Jun 17, 2019

very good!!

von Mohammed R

Aug 07, 2015

the audio is sometimes noisy, but everything else is perfect, thanks a lot

von Joy F Y

Aug 07, 2015

It's very useful

von Saiful I A

Aug 07, 2015

Very Nice

von Weixiang Z

Apr 03, 2018

Very nice course,. Give a fundamental knowledge of machine learning in a clear, logic and easy-to-understand way. Suitable for those who has relatively weak background of math and statistics to learn.

von Yashendra M

Jul 24, 2019

A great course to opt for. Learned many new things. All of them were relatable to the daily life of internet. A well made course. Thanks Andrew Ng for this course. You are a great teacher. Loved the coding in this. All those algorithms were awesome.

von Brian

Aug 07, 2015

It's amazing, I can learn fanstastic stuff through this free course. There is no boundary. I could implement the machine learning code , and understand well. Thank you so much.

von Tushar T

Aug 07, 2015

Its amazing course, very detailed and good explanation of each algorithm. Mr Andrew NG has good teaching skills, I am glad that I came across this course. Thanks Cousera. :)

von Francisco M M

Feb 24, 2017

Excelente curso! No solo explica detalladamente el sustento teórico de los diversos temas, además propone ejemplos aplicativos sencillos que ayudan a una mayor comprensión. Y por si fuera poco toca temas algo complejos pero sin perder la excelente pedagogía.

Lo recomiendo!

von Ajay T

Jul 29, 2019

Excellent course. Discussion forum help from the mentors was super in the first half of the course but towards the end the mentors did not participate

von Anup B D

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.

von Zilin L

Jun 07, 2019

几乎没有数学要求,老少咸宜。

编程作业设计非常用心,专注于让学生完成核心人物。

好评!

von BS

Sep 22, 2019

Great comprehensive way to break into machine learning as a subject. I feel confident that this has provided the foundation I need to further explore the subject either by reading up on new topics in the machine learning area or thinking about how to apply machine learning concepts to my personal projects.

von Naveen K

Sep 19, 2019

One of the best Machine learning course :) Andrew's way of teaching is really a masterpiece :) Thank you Coursera

von Vikrant K

Aug 30, 2019

It's so wonderful that it can't be explained by the words and at the same time i am very sad that Ng sir has left us . i just love Ng sir , He is so wonderful person and teacher that can't be explained by the words .It's quite bit a big dream but i am dreaming of some day in the future where i am working with Ng sir on some machine learning problem and he is guiding me as he is doing now . I just love the course and also the mentors Mr. Neil Ostrove and Mr. Tom he had helped us to complete this course and assignment and also solved my useless something baby problems more carefully and i will help other student as guided by Ng sir in completing this course smoothely . and that's all . at the last i want to tell I just fall in love with Ng sir and coursera and the team . i have a big dream of meeting that my favourite Ng sir on some day.

Thank you

von Vincent C

Sep 25, 2019

After finishing the course, I feel much more confident in pursuing more advanced machine learning. The course teaches everything intuitively and in detail but maybe it could use some improvement to achieve perfection. It would be better if the course could provide pointers to some of the topics beyond the scope of the course such as the derivation of the back propagation, svm, pca, etc. Because often times when you search for derivations they might not be very useful for your levels, if course could provide some good references as some lecture notes after the video would be great for the students to gain even more solid groundings of the things behind the hood

Super thanks and thumbs up