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机器学习, 斯坦福大学

94,074 个评分
23,802 个审阅


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....


创建者 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..

创建者 ML

Aug 19, 2017

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.


22,952 个审阅

创建者 Ramakrishna Surya

Feb 21, 2019

Loved the class. Someday I hope to meet Mr. Ng to thank him for the service that he has to thousands of students across the planet. I as a material scientist will pursue this as a hobby and possibly integrate machine learning into material science.

创建者 Parth Rameshbhai Chandgadhiya

Feb 21, 2019

Ng_Theme_park = E( most thrilling rides for hungry Machine Learners ).

创建者 Berkant

Feb 21, 2019

I found the course quite enjoyable. Thank you for the work and effort. As a suggestion: what I think is missing (or perhaps not clear for me) are directions to read and learn the mathematical details of all the algorithms that were discussed. I can probably do it on my own but if there were pointers I would have been much happier. As is at some points I was considering dropping the course. It was the programming assignments that kept me going on.

创建者 Arvind Sharma

Feb 21, 2019

Firstly I would like to thank the Coursera for allowing me to take this class on Financial Aid!

This course gave the perfect start to the world of Machine Learning!

I would like to sincerely thank Andrew NG for making this course awesome !

thanks a lot ! enjoyed it and learnt a lot !!!!! thank you!

创建者 Ramki Pitchala

Feb 21, 2019

I learned an absolute lot of things in this class!

创建者 Erdem TOSUN

Feb 21, 2019

incredibly useful. Thank you!!

创建者 Carlos Abadi

Feb 21, 2019

Andrew is a fantastic teacher!

创建者 Mehul jain

Feb 21, 2019

As a beginner in machine learning,it is a course that one must take

创建者 Arash Saeidpour

Feb 21, 2019

A fantastic foundational ML course, highly recommended!

创建者 Murat Erdogan

Feb 21, 2019

I joined this course with very few knowledge (just Neural network course at student time more then 10 years before). It was a really big pleasure, a lot of fun and also inspirational to get throught this course with Andrew Ng. I learned so much about machine learning in this course with a lot of good practices and exercises. This course inspired me to do much more in that area of AI.

Thank you very much Andrew Ng.