Linear Algebra for machine learning and Data science.

Linear Algebra for machine learning and Data science., available at $39.99, has an average rating of 3.79, with 80 lectures, based on 7 reviews, and has 75 subscribers.

You will learn about Classification of matrices Matrix algebra like addition, subtraction and multiplication Trace of matrix,transpose of matrix symmetric and skew symmetric matrix Real and complex matrices Orthogonal matrix Determinant of Matrix: Minors, Cofactors of matrix Invertible and non Invertible matrix Inverse of matrix: adjoint of matrix Finding the Rank of matrices Row Echlon form Relation between Rank and vectors of matrix Linearly independant and dependant vectors Vector space: Dimension, Basis, Span and Nullity Solving simultaneous system of linear Equations Homogeneous and non homogeneous system of equation Eigen values and their carrosponding Eigen vectors Properties of eigen values and eigen vector cayley hamilton theorem This course is ideal for individuals who are Students enrolled or planning to enroll in Linear Algebra class, and who want to excel in it or Anyone who needs Linear Algebra as a prerequisite for Machine Learning, Deep Learning, Artificial Intelligence, Computer Programming, Computer Graphics and Animation, Data Analysis, etc. or Engineers, Scientists and Mathematicians who want to work with Linear Systems and Vector Spaces or Professionals who need a refresher in Math, especially Algebra and Linear Algebra It is particularly useful for Students enrolled or planning to enroll in Linear Algebra class, and who want to excel in it or Anyone who needs Linear Algebra as a prerequisite for Machine Learning, Deep Learning, Artificial Intelligence, Computer Programming, Computer Graphics and Animation, Data Analysis, etc. or Engineers, Scientists and Mathematicians who want to work with Linear Systems and Vector Spaces or Professionals who need a refresher in Math, especially Algebra and Linear Algebra.

Enroll now: Linear Algebra for machine learning and Data science.

Summary

Title: Linear Algebra for machine learning and Data science.

Price: $39.99

Average Rating: 3.79

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Classification of matrices
Matrix algebra like addition, subtraction and multiplication
Trace of matrix,transpose of matrix
symmetric and skew symmetric matrix
Real and complex matrices
Orthogonal matrix
Determinant of Matrix: Minors, Cofactors of matrix
Invertible and non Invertible matrix
Inverse of matrix: adjoint of matrix
Finding the Rank of matrices
Row Echlon form
Relation between Rank and vectors of matrix
Linearly independant and dependant vectors
Vector space: Dimension, Basis, Span and Nullity
Solving simultaneous system of linear Equations
Homogeneous and non homogeneous system of equation
Eigen values and their carrosponding Eigen vectors
Properties of eigen values and eigen vector
cayley hamilton theorem

Who Should Attend

Students enrolled or planning to enroll in Linear Algebra class, and who want to excel in it
Anyone who needs Linear Algebra as a prerequisite for Machine Learning, Deep Learning, Artificial Intelligence, Computer Programming, Computer Graphics and Animation, Data Analysis, etc.
Engineers, Scientists and Mathematicians who want to work with Linear Systems and Vector Spaces
Professionals who need a refresher in Math, especially Algebra and Linear Algebra

Target Audiences

Students enrolled or planning to enroll in Linear Algebra class, and who want to excel in it
Anyone who needs Linear Algebra as a prerequisite for Machine Learning, Deep Learning, Artificial Intelligence, Computer Programming, Computer Graphics and Animation, Data Analysis, etc.
Engineers, Scientists and Mathematicians who want to work with Linear Systems and Vector Spaces
Professionals who need a refresher in Math, especially Algebra and Linear Algebra

Why study linear algebra?

Linear algebra is vital in multiple areas of science in general. Because linear equations are so easy to solve, practically every area of modern science contains models where equations are approximated by linear equations (using Taylor expansion arguments) and solving for the system helps the theory develop. Beginning to make a list wouldn’t even be relevant ; you and I have no idea how people abuse of the power of linear algebra to approximate solutions to equations. Since in most cases, solving equations is a synonym of solving a practical problem, this can be VERY useful. Just for this reason, linear algebra has a reason to exist, and it is enough reason for any scientific to know linear algebra.

More specifically, in mathematics, linear algebra has, of course, its use in abstract algebra ; vector spaces arise in many different areas of algebra such as group theory, ring theory, module theory, representation theory, Galois theory, and much more. Understanding the tools of linear algebra gives one the ability to understand those theories better, and some theorems of linear algebra require also an understanding of those theories ; they are linked in many different intrinsic ways.

Outside of algebra, a big part of analysis, called functional analysis, is actually the infinite-dimensional version of linear algebra. In infinite dimension, most of the finite-dimension theorems break down in a very interesting way ; some of our intuition is preserved, but most of it breaks down. Of course, none of the algebraic intuition goes away, but most of the analytic part does ; closed balls are never compact, norms are not always equivalent, and the structure of the space changes a lot depending on the norm you use. Hence even for someone studying analysis, understanding linear algebra is vital.

In linear algebra, we will learn, Classification of matrices, Matrix algebra like addition,subtraction and multiplication, transpose of matrix, symmetric and skew symmetric matrix, Real and complex matrices, Determinant of Matrix: Minors, Cofactors of matrix, Inverse of matrix: adjoint of matrix, Finding the Rank of matrices, Row Echlon form, Relation between Rank and vectors of matrix, Linearly independant and dependant vectors, Vector space: Dimension, Basis, Span and Nullity, Solving system of linear Equations, Homogeneous and non homogeneous system of equation, Eigenvalues and their carrosponding Eigenvectors, Properties of eigenvalues and eigenvectors, cayley hamilton theorem.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Linear algebra and matrices

Chapter 2: Classification of Matrices

Lecture 1: Classification of Matrices (part 1)

Lecture 2: Classification of Matrices (part 2)

Lecture 3: Classification of Matrices (part 3)

Chapter 3: Matrix algebra and various operation on Matrix

Lecture 1: Addition and Subtaction of Matrices

Lecture 2: Matrix multiplication and its properties

Lecture 3: Example 1 on matrix multiplication

Lecture 4: Example 2 on matrix multiplication

Lecture 5: Trace of matrix

Lecture 6: Transpose of a Matrix

Lecture 7: Transposed conjugate

Chapter 4: Classification of Real and Complex matrix

Lecture 1: Symmetric and skew symmetric matrix and orthogonal matrix

Lecture 2: Hermitian and skew hermitian matrix and Unitary marix

Chapter 5: Determinant and Inverse of Matrices

Lecture 1: Minors of matrix

Lecture 2: Cofactors and Determinant of matrices

Lecture 3: Determinant example 1

Lecture 4: Determinant example 2

Lecture 5: Properties of Determinants

Lecture 6: Determinant example 3

Lecture 7: determinant example 4

Lecture 8: determinant example 5

Lecture 9: determinant example 6

Lecture 10: determinant example 7

Lecture 11: Adjoint and Inverse of matrix

Lecture 12: Example 1 on inverse of matrix

Lecture 13: Example 2 on inverse of matrix

Lecture 14: Example 3 on inverse of matrix

Lecture 15: Eaxample 4 on orthogonal and inverse of matrix

Lecture 16: Example 5 on Inverse

Lecture 17: Determinant of adjoint

Chapter 6: Rank and Vector space

Lecture 1: Rank of Matrices

Lecture 2: example 1 on Rank of matrix

Lecture 3: Row echelon form

Lecture 4: Example 2 Rank of matrix

Lecture 5: Example 3 rank of matrix

Lecture 6: Example 4 on rank of adjoint of matrix

Lecture 7: Linearly independent and Linearly dependent vectors

Lecture 8: Relation between Rank and Vectors of matrix

Lecture 9: Example 1 on linearly independent vector

Lecture 10: Example 2 linearly independent vector

Lecture 11: Example 3 on invertible and non invertible matrix

Lecture 12: Orthogonal vector

Lecture 13: Vector space, dimension, basis and span

Lecture 14: Cheack span

Lecture 15: Nullity of matrix

Chapter 7: Solving simultaneous equations using matrices

Lecture 1: what is considered consistent and inconsistent system of equation?

Lecture 2: Example 1 nature of solution

Lecture 3: Non homogeneous system of equations

Lecture 4: non homogeneous system of linear equations example 1

Lecture 5: Example 2 nature of solution

Lecture 6: Example 3 nature of solution

Lecture 7: example 4 nature of solution

Lecture 8: cramer's rule

Lecture 9: Homogeneous system of linear equation

Lecture 10: homogenous equation example 1

Lecture 11: homogenous equation example 2

Lecture 12: homogenous equation example 3

Lecture 13: homogenous equation example 4

Chapter 8: Eigenvalues and Eigenvectors

Lecture 1: Eigenvalues

Lecture 2: Eigenvalues example 1

Lecture 3: Eigenvectors

Lecture 4: Repeated Eigenvalues and carrosponding Eigenvectors

Lecture 5: Eigenvalues and Eigenvectors example 2

Lecture 6: Eigenvalues and Eigenvectors example 3

Lecture 7: Eigenvalues and Eigenvectors example 4

Lecture 8: Eigenvalues and Eigenvectors example 5

Lecture 9: Eigenvalues and Eigenvectors example 6

Lecture 10: Properties of Eigenvalues and Eigenvectors

Lecture 11: Eigenvalues and Eigenvectors example 7

Lecture 12: Eigenvalues and Eigenvectors example 8

Lecture 13: Eigenvalues and Eigenvectors example 9

Lecture 14: Eigenvalues and Eigenvectors example 10

Lecture 15: Eigenvalues and Eigenvectors example 11

Lecture 16: Eigenvalues and Eigenvectors example 12

Lecture 17: Eigenvalues and Eigenvectors example 13

Lecture 18: Eigenvalues and Eigenvectors example 14

Chapter 9: cayley hamilton theorem

Lecture 1: cayley hamilton theorem

Lecture 2: cayley hamilton theorem example 1

Lecture 3: cayley hamilton theorem example 2

Lecture 4: cayley hamilton theorem example 3

Instructors

Vishwesh Singh
Electrical and Electronics engineer

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4 stars: 1 votes
5 stars: 3 votes

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