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Avis et commentaires pour l'étudiant pour Apprentissage automatique par Université de Stanford

4.9
121,373 notes
29,802 avis

À propos du cours

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....

Meilleurs avis

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.

PM

Jul 14, 2019

This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

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201 - 225 sur 28,921 Examens pour Apprentissage automatique

par Rohan R

Jan 28, 2019

Andrew Ng is an excellent and caring professor.

par TanBui

Jan 28, 2019

An extremely basic but not easy experience about Machine Learning concepts. It has been very helpful for me to have a better grasp in my future Deep Learning careers.

par Florian P

Jan 28, 2019

Very intiutitve. Good lecture materials, nice tutorials.

par Alex L

Jan 28, 2019

excellent!

par Michael G B

Jan 29, 2019

.

par Sumant k

Jan 27, 2019

great course

par Nikhil K

Jan 28, 2019

kindly cover more algorithms so it would be grateful for us

par Marcelo A X

Jan 28, 2019

Such an awesome course!! Te content is assertive and broad. The professor teach complex concepts in a very clear and simple way. The exersices are rich and complete and not much complex for a student. This is a mandatory course for AI students.

par Arvind G

Jan 28, 2019

Very educational. I learned a lot, and feel that I have now quite a thorough understanding of the basic principles of machine learning.

par Juan C

Jan 29, 2019

Es muy bueno. Permite tener un conocimiento completo del tema,

par 李大权

Jan 29, 2019

收获很多

par Yang Y

Jan 27, 2019

Fundamental and useful!

par JOAQUIM M

Jan 27, 2019

Very structured and complete. The exercises are very useful

par Ijoline H

Jan 27, 2019

Very clear! Thanks Andrew Ng.

par Luke W

Jan 28, 2019

Andrew老师的课程由浅入深,讲解的很细致,受益匪浅

par 闻海宾

Jan 28, 2019

通俗易懂,非常棒!

par Dale

Jan 28, 2019

Thank you very much for offering this wonderful course for us!

par Christopher C

Jan 29, 2019

Such a great course from a great teacher. Really recommend this one to absolute beginners.

par Cristian G

Jan 27, 2019

Un curso maravilloso, muy completo y el tutor explica muy bien, los ejercicios son practicos y ayudan a afianzar el conocomiento, hay que dedicar tiempo y tomarlo muy en serio.

par Lucian N

Jan 27, 2019

a very good step towards learning ML. thanks!

par David

Jan 28, 2019

concretely

par dhrumil r

Jan 28, 2019

A very informative course which is helpful in understanding the basic of machine lesrning and how to implement it

par ainavilli v

Jan 28, 2019

what a great mentor you are .thank you Andrew Ng for this great stuff

par Gustavo M V R

Jan 28, 2019

I decided to take this course moved by curiosity, and I didn't expect it to be so helpful for my work. However, Professor Andrewn Ng exeeded my expectations, and the content of this course was not only interesting for my research, but it opened new ways in which machine learnig can be an useful tool for me. I am a young astrophysicist, and I could inmediately use the concepts of linear regression and neural networks to fit complex functions describing different states of the atom, and to classify automatically different kind of galaxies.

par Muhammad U

Jan 28, 2019

Great