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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-linear-algebra-matrices/
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**课程名称：** 线性代数入门：矩阵 (Introduction To Linear Algebra MATRICES) **课程概述：** 本课程旨在通过清晰易懂的方式，帮助学习者掌握线性代数中的核心概念，特别是矩阵。矩阵作为线性代数理论的基石，在机器学习、数据科学、计算机科学和电子工程等领域有着广泛的应用。 本课程包含超过48个讲座视频，涵盖从矩阵基础到更高级概念的详尽解释，并配有超过45个带有详细解答的练习题，帮助学习者巩固理解。 **课程内容结构：** 1. **矩阵导论** 2. **矩阵的类型**：列矩阵、行矩阵、对角矩阵、三角矩阵、零矩阵、单位矩阵等。 3. **矩阵与行列式的区别** 4. **矩阵运算**：加法、减法、乘法、转置、复共轭、转置共轭。 5. **各类特殊矩阵**：幂等矩阵、周期矩阵、幂零矩阵、对合矩阵、置换矩阵、对称矩阵、反对称矩阵、厄米矩阵、反厄米矩阵。 6. **方阵的伴随矩阵** 7. **初等行变换与列变换** 8. **矩阵的逆** 9. **矩阵的阶梯形和标准形** 10. **矩阵的秩** 11. **线性方程组的求解** 12. **反射矩阵** 13. **角度 $\theta$ 的旋转矩阵** **课程价值：** 本课程将为学习者后续深入学习线性代数的高级主题（如特征值与特征向量、奇异值分解、线性规划等）打下坚实的基础。
HOW INTRODUCTION TO LINEAR ALGEBRA MATRICES IS SET UP TO MAKE COMPLICATED LINEAR ALGEBRA EASY This course deals with concepts required for the study of Machine Learning and Data Science. Matrices is a fundamental of the Theory of Linear Algebra. Linear Algebra is used in Machine Learning, Data Science, Computer Science and Electrical Engineering. This 48+ lecture course includes video explanations of everything from Fundamental of Matrices, and it includes more than 45+ examples (with detailed solutions) to help you test your understanding along the way. Introduction To Linear Algebra MATRICES is organized into the following sections: Introduction to MatricesTypes of Matrices {Column Matrix, Row Matrix, Diagonal Matrix, Triangular Matrix, Null Matrix, Identity Matrix} Difference between a Matrix and a DeterminantOperations on Matrices {Addition, Subtraction, Multiplication, Transpose, Complex Conjugate, Transpose Conjugate}Various Kinds Of Matrices {Idempotent, Periodic, Nilpotent, Involutory, Permutation, Symmetric, Skew-Symmetric, Hermitian, Skew-Hermitian Matrix}Adjoint of a Square MatrixElementary Row and Column TransformationInverse of a MatrixEchelon Form and Normal Form of a MatrixRank of a MatrixSolution of Simultaneous Linear EquationsThe Reflection MatrixRotation Through an Angle ThetaThis course will act as a pre-requisite for advance courses in Linear Algebra like Eigen Values and Eigen Vectors, Singular Value Decomposition, Linear Programming and others.
