你是否好奇数据可以告诉你什么？你是否想在关于机器学习促进商业的核心方式上有深层次的理解？你是否想能同专家们讨论关于回归，分类，深度学习以及推荐系统的一切？在这门课上，你将会通过一系列实际案例学习来获取实践经历。在这门课结束的时候，
Recommendations from known user/item features
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您将学习的技能
Python Programming, Machine Learning Concepts, Machine Learning, Deep Learning
审阅
DS
Sep 28, 2015
Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!
GG
Jun 05, 2017
This course is very helpful for people who are novice in machine learning. The course uses Graphlab Create which is different from scikit or R-libraries, but the tool(Graphlab) is excellent to use.
从本节课中
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Ever wonder how Amazon forms its personalized product recommendations? How Netflix suggests movies to watch? How Pandora selects the next song to stream? How Facebook or LinkedIn finds people you might connect with? Underlying all of these technologies for personalized content is something called collaborative filtering. <p>You will learn how to build such a recommender system using a variety of techniques, and explore their tradeoffs.</p> One method we examine is matrix factorization, which learns features of users and products to form recommendations. In an iPython notebook, you will use these techniques to build a real song recommender system.
教学方
Carlos Guestrin
Amazon Professor of Machine Learning
Emily Fox
Amazon Professor of Machine Learning
