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Introduction To Linear Algebra |MATRICES|

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Partner: Udemy
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Description: 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.
Category: Teaching & Academics > Math > Linear Algebra
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Price: 119.99
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Source: Impact
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