Introduction To Linear Algebra |MATRICES|

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 Matrices

• Types of Matrices {Column Matrix, Row Matrix, Diagonal Matrix, Triangular Matrix, Null Matrix, Identity Matrix}

• Difference between a Matrix and a Determinant

• Operations 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 Matrix

• Elementary Row and Column Transformation

• Inverse of a Matrix

• Echelon Form and Normal Form of a Matrix

• Rank of a Matrix

• Solution of Simultaneous Linear Equations

• The Reflection Matrix

• Rotation Through an Angle Theta

  
  

  This 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.