畏者无勇

八零后,IT民工,足球迷。关注大数据、机器学习

北京 海淀区

感兴趣的主题: 足球迷 IT民工 80后

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Machine Learning (Coursera) 28 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/ml

简介: Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

The Hardware/Software Interface (Coursera) 7 个评论 关注

开始时间: 06/30/2014 持续时间: 8 weeks

主页: https://www.coursera.org/course/hwswinterface

简介: Examines key computational abstraction levels below modern high-level languages. From Java/C to assembly programming, to basic processor and system organization.

Introduction to Recommender Systems (Coursera) 3 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/recsys

简介: This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!

Mathematical Methods for Quantitative Finance (Coursera) 2 个评论 关注

开始时间: 06/01/2015 持续时间: 8 weeks

主页: https://www.coursera.org/course/mathematicalmethods

简介: Mathematical Methods for Quantitative Finance covers topics from calculus and linear algebra that are fundamental for the study of mathematical finance. Students successfully completing this course will be mathematically well prepared to study quantitative finance at the graduate level.

Learning From Data (edX) 1 个评论 关注

开始时间: 09/25/2014 持续时间: 10 weeks

主页: https://www.edx.org/course/learning-data-caltechx-cs1156x

简介: Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

Neural Networks for Machine Learning (Coursera) 5 个评论 关注

开始时间: 待定 持续时间: 8 weeks

主页: https://www.coursera.org/course/neuralnets

简介: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

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