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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-algebra-for-data-science-and-machine-learning-using-r/
课程评论：没有评论
**课程名称：** 线性代数在数据科学与机器学习中的应用 (使用 R 语言) **课程概述：** 本课程旨在帮助学员深入理解数据科学和机器学习背后的线性代数数学原理。线性代数是数据科学和机器学习的基石。课程内容涵盖了线性代数的各个核心主题，并结合 R 语言代码演示和实际问题解决，提供丰富的实践经验。 **课程内容要点：** * **导论：** 探讨线性代数的概念及其在数据科学和机器学习中的重要性。 * **R 语言入门：** 学习如何设置 R 环境，为实践操作打下基础。 * **核心主题：** * 向量及其运算 * 矩阵及其运算 * 行列式与逆矩阵 * 线性方程组的求解 * 范数与基向量 * 线性无关性 * 矩阵分解 * 正交性 * 特征值与特征向量 * 奇异值分解 (SVD) * **实践应用：** 每个主题都包含 R 语言代码演示和具体问题的解决示例。 * **工具使用：** 学习使用 R 语言的 `pracma` 和 `matrixcalc` 等库，这些库提供了丰富的矩阵计算和线性代数问题求解函数。 **学习收获：** 通过本课程的学习，学员将能够掌握线性代数的关键概念，理解它们在数据科学和机器学习任务中的作用，并能够运用 R 语言进行相关的计算和分析。
This course will help you in understanding of the Linear Algebra and math's behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.There're tons of topics in this course. To begin the course:We have a discussion on what is Linear Algebra and Why we need Linear AlgebraThen we move on to Getting Started with R, where you will learn all about how to setup the R environment, so that it's easy for you to have a hands-on experience.Then we get to the essence of this course;Vectors & Operations on VectorsMatrices & Operations on MatricesDeterminant and InverseSolving Systems of Linear EquationsNorms & Basis VectorsLinear IndependenceMatrix FactorizationOrthogonalityEigenvalues and EigenvectorsSingular Value Decomposition (SVD)Again, in each of these sections you will find R code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the R's pracma, matrixcalc library which contains numerous functions for matrix computations and solving Linear Algebric problems. So, let's get started….
