Introduction To Linear Algebra |MATRICES|

Fundamental Course in Linear Algebra for Machine Learning, Data Science, Computer Science and Electrical Engineering

Ratings: 4.00 / 5.00




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 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.

What You Will Learn!

  • Matrices Definition
  • Types of Matrices
  • Difference between a Matrix and a Determinant
  • Operations on Matrices
  • Various Kinds Of Matrices
  • 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

Who Should Attend!

  • Current Linear Algebra students, or students about to start Linear Algebra who are looking to get ahead
  • Students of Machine Learning, Data Science, Computer Science, Electrical Engineering , as Linear Algebra is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering
  • Anyone who wants to study Matrices for fun after being away from school for a while.