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Python (Coursera专项课程) 5 个评论 关注 开始时间: 04/22/2022 持续时间: free 主页: http://www.codecademy.com/zh/tracks/python 简介: Learn the fundamentals of Python and dynamic programming. |
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Machine Learning (CourseraArchive) 29 个评论 关注 开始时间: 04/22/2022 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. |
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Introduction to Statistics (Udacity) 3 个评论 关注 开始时间: 04/22/2022 持续时间: 自主 主页: https://www.udacity.com/course/st101 简介: Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics. |
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计算广告学 (CourseraArchive) 5 个评论 关注 开始时间: 04/22/2022 持续时间: 8 weeks 主页: http://study.163.com/course/courseMain.htm?courseId=321007 简介: 本课程从计算广告各个阶段所遇到的市场挑战为出发点，清晰解释了广告市场纷繁复杂的产品逻辑，并且对其中的关键技术和算法做了深入的探讨。依次介绍了合约广告系统(Agreement-based Advertising)，竞价广告系统(Auction-based Advertising)，广告交易市场(Ad Exchange)等重要课题。 |
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CVX101: Convex Optimization (Stanford Online) 1 个评论 关注 开始时间: 04/22/2022 持续时间: 未知 主页: https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about 简介: This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance. |
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Natural Language Processing (CourseraArchive) 7 个评论 关注 开始时间: 04/22/2022 持续时间: 10 weeks 主页: https://www.coursera.org/course/nlangp 简介: Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing. |
