Mathematics for Machine Learning: Linear Algebra

开始时间: 09/02/2018 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/linear-algebra-machine-learning

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课程详情

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

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课程简介

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and

课程标签

数据科学 机器学习 PCA 多变量微积分 微积分 帝国理工学院 主成分分析 线性代数 数学 机器学习数学基础 面向机器学习的数学 伦敦帝国理工学院

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