Python中用于数据科学和机器学习的线性代数视频教程

向量、矩阵、线性方程组、因式分解、特征向量、最小平方、奇异值分解，本课程将帮助你理解数据科学和机器学习背后的线性代数和数学。线性代数是数据科学和机器学习的基础部分。本课程包括线性代数每个主题的课程+线性代数概念或主题的代码或实现。这门课有大量的话题。课程开始时:我们讨论了什么是线性代数以及为什么我们需要线性代数，然后我们继续学习Python入门，在这里您将了解如何设置Python环境，以便您可以轻松地进行实践。然后我们就进入了这门课的本质。向量和向量。向量矩阵的运算。矩阵行列式的运算和线性方程组的逆解。基础向量线性无关矩阵分解正交特征值和特征奇异值分解(SVD)同样，除了线性代数的理论概念之外，在每一节中，您都可以找到Python代码演示和已解决的问题。您还将学习如何使用Python的numpy库，该库包含许多用于矩阵计算和解决线性代数问题的函数。那么，让我们开始吧…Linear Algebra for Data Science & Machine Learning in Python (2023)

由Syed Mohiuddin创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子学习|语言:英语|时长:150节课(9小时51分钟)|大小:1.44GB 含课程文件

你会学到什么
线性代数基础
向量和矩阵的应用及其Python实现
向量和矩阵的运算及其Python实现
求解线性方程组及其Python实现
矩阵分解及其在Python中的实现
特征值、特征向量的计算
奇异值分解及其在Python中的实现
特征分解及其在Python中的实现

要求
你应该熟悉数学基础
所有线性代数概念的实现都是用Python编写的，所以熟悉Python将是一个额外的优势

这门课程是给谁的
任何对线性代数如何用于机器学习感到好奇的人
任何想要理解数据科学背后的数学和线性代数的人
任何想要为部署机器学习技术开发基础的人

Vectors, Matrices, Systems of Linear Equations, Factorization, Eigenvectors, Least Squares, SVD

What you’ll learn
Fundamentals of Linear Algebra
Applications of Vectors and Matrices with implementation in Python
Operations on Vectors and Matrices with implementation in Python
Solve Systems of Linear Equations and implementation in Python
Matrix Factorization and implementation in Python
Computation of Eigenvalues, Eigenvectors
Singular Value Decomposition with its implementation in Python
Eigen Decomposition with their implementation in Python

Requirements
You should have familiarity with fundamentals of Maths
All the implementation of Linear Algebra concepts are in Python, so familiarity with Python will be an added advantage

Description
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 Python, where you will learn all about how to setup the Python 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 Python code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the Python’s numpy library which contains numerous functions for matrix computations and solving Linear Algebric problems.So, let’s get started….

Who this course is for
Anyone who is curious about how Linear Algebra is used in Machine Learning
Anyone who wants to understand Maths and Linear Algebra behind Data Science
Anyone who wants to develop fundamental foundations for deployment of Machine Learning Techniques