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Apprentissage automatique, Université de Stanford

4.9
97,019 notes
24,382 avis

À propos de ce 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

par 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.

par SB

Sep 27, 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

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23,551 avis

par

Mar 23, 2019

Awesome and in depth explanation of every concept.Recommended to all ML enthusiast

par Yongtao SUN

Mar 23, 2019

非常有用的机器学习入门课程!

par Nicholas Pacey

Mar 23, 2019

This course taught me the overall aspects of machine learning. I've spent two years searching for courses on machine learning, but of all the courses I've looked through, this one is the only one I truly understood. Thank you, Andrew Ng.

par Dana Miller

Mar 23, 2019

This course makes use of Linear Algebra Techniques in the homework problems. This seems to cover the key areas of this field and, introduces the concepts, and shows their practical application.

par Yuri Delos Santos Rellosa

Mar 23, 2019

Got me started with machine learning. Very easy to follow.

par WataruKuroshima

Mar 23, 2019

gごおd

par Anirban Bhattacharya

Mar 23, 2019

Excellent course . Recommended for all new Product developers.

par Gaetan Jamar

Mar 23, 2019

Awesome class and teacher, thank you Pr Andrew Ng !!

par KARTHIK P

Mar 23, 2019

Brilliantly put through and taught by Andrew Ng. I would love to take more of his classes.

par Mayank M Saini

Mar 23, 2019

Best Course for Machine Learning