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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning-in-python-f/
课程评论：没有评论
Coursera 课程《Python 数据科学与机器学习线性代数》课程总结 本课程旨在帮助学习者深入理解数据科学与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础。课程内容涵盖了线性代数的各个主题，并结合 Python 代码实现，提供理论与实践相结合的学习体验。 课程开篇将介绍线性代数是什么以及为何需要它。随后，将深入学习 Python 环境的搭建与使用，为后续的实践操作打下基础。 核心内容包括： * 向量及其运算 * 矩阵及其运算 * 行列式与逆矩阵 * 求解线性方程组 * 范数与基向量 * 线性无关 * 矩阵分解 * 正交性 * 特征值与特征向量 * 奇异值分解 (SVD) 在每个主题章节中，除了理论概念讲解外，都将包含 Python 代码演示和已解决问题的实例。此外，课程还将重点教授如何使用 Python 的 NumPy 库，该库提供了丰富的矩阵计算和解决线性代数问题的函数。 总之，本课程致力于为学习者提供一个扎实的线性代数基础，并通过 Python 代码实践，使其能够融汇贯通，应用于数据科学和机器学习领域。
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….
