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返回到 机器学习

机器学习, 斯坦福大学

93,967 個評分
23,781 個審閱


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....


創建者 JS

Jun 17, 2017

Everything is taught from basics, which makes this course very accessible- still requires effort, however will leave you with real confidence and understanding of subjects covered. Great teacher too..

創建者 EJ

Mar 27, 2018

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.


22,924 個審閱

創建者 Iyappan S

Feb 20, 2019

vry good


Feb 20, 2019

Soooooooooooo great lecture that envisions my probability as a data scientist.

創建者 Thong Dong Le Ba

Feb 20, 2019

I would love to see more rigorous Mathematics

創建者 SStone

Feb 20, 2019

很高兴通过coursera这个平台,听取Andrew Ng教授的machine learning 课程,对机器学习有了入门的认识,后面多加回顾,继续学习教授发布的相关课程,期待更深入的学习。

創建者 Romil Lodaya

Feb 20, 2019

All concepts are cleared very nicely.

創建者 陈晓伟

Feb 20, 2019

I ready become better in machine learning after learning in this course

創建者 Kanimozhi Kalaichelvan

Feb 20, 2019

Excellent explanation by Andrew.

Love the course. Would be delighted if more courses on other topics are available :-)

創建者 Monil Nisar

Feb 20, 2019

Awesome course for students and even professor who want to pursue Data science and learn about basics of Machine learning. Although this course seems outdated, but it is preety good for begineers. The followup course should be the one available on ''

創建者 Baejun Park

Feb 20, 2019

It is great course to understand ML as a beginner and to know ML application.

Thank you very much!!!!

創建者 钟雨辰

Feb 20, 2019

Thank you,Andrew Ng