Linear AlgebraA masterclass course for learners

-From Basics to advance level: recommended for university students, software engineers, data scientists, GATE & ESE exam
Linear AlgebraA masterclass course for learners
File Size :
2.37 GB
Total length :
9h 18m


Sm Bakhteyar


Last update




Linear AlgebraA masterclass course for learners

What you’ll learn

Basics of Matrices, addition and multiplication of matrices
Rank of matrix
system of linear equations AX=B, AX=0
Eigen values and Eigen vectors, Diagonalization A=PDP−1
Special matrices
LU Decompositions
Minimal Polynomial
Trace of matrix, Diagonal matrix, upper/lower/diagonal triangular matrix
Inverse of matrix, adjoint of A matrix, Co-factor matrix , transpose of matrix
shortcut to find the determinant and inverse of a matrix
determinant, inverse, adjacent, and transpose properties
Determinant of matrix A of nxn order
Row echelon form, general form of Row echelon form
consistent, in-consistent, homogeneous and non homogeneous matrices
Cramer’s rule method
Gaussian elimination method, augmented matrix
system of homogeneous equations, linearly dependent and independent vectors
number of solutions, trivial and non trivial solutions, consistent and in-consistent solutions, infiniately many solutions
Cayley Hamilton theorem, shortcut method to find characteristic equations for matrices

Linear AlgebraA masterclass course for learners


You should be comfortable with the Fundamentals of Math, like arithmetic (addition, subtraction, multiplication, division) of positive and negative numbers, fractions, and decimals.
You should be comfortable with Algebra, like equation solving, graphing, and factoring, plus exponents and roots.
You’ll only need Fundamentals and Algebra to solve Linear Algebra problems, so if you have that foundation, you’ll be well prepared for this course.


To help you assess your understanding as you go, this course contains quizzes (with solutions!) and bonus workbooks with more practise problems. It also contains written and video explanations of all topics related to linear algebra. A common subject in the college mathematics curriculum is linear algebra. Typically, sophomore-year students enrol in it. For those majoring in math, physics, engineering, statistics, or economics, linear algebra is a prerequisite. Unknown quantities can often be determined using linear algebra. Among linear algebra’s practical uses are the following: to calculate time, distance, or speed. employed by linear maps to translate a three-dimensional image into a two-dimensional plane. Because it enables the modelling of several natural events, linear algebra is also used in the majority of scientific and engineering sectors.The introduction of linear algebra in the West dates back to the year 1637, when René Descartes develop the concept of coordinates under a geometric approach, known today as Cartesian geometry.Linear algebra is easier than elementary calculus. In Calculus, you can get by without understanding the intuition behind theorems and just memorizing algorithms, which won’t work well in the case of linear algebra. By understanding the theorems in linear algebra, all questions can be solved


Section 1: Linear Algebra

Lecture 1 Linear Algebra Part 1

Lecture 2 Linear Algebra Part 2

Lecture 3 Linear Algebra Part 3

Lecture 4 Linear Algebra Part 4

Lecture 5 Linear Algebra Part 5

Lecture 6 Linear Algebra Part 6

Lecture 7 Linear Algebra Part 7

Lecture 8 Linear Algebra Part 8

Lecture 9 Linear Algebra Part 9

Lecture 10 Linear Algebra Part 10

Lecture 11 Linear Algebra Part 11

Lecture 12 Linear Algebra Part 12

Lecture 13 Linear Algebra Part 13

Lecture 14 Linear Algebra Part 14

Lecture 15 Linear Algebra Part 15

Lecture 16 Linear Algebra Part 16

Lecture 17 Linear Algebra Part 17

Current Linear Algebra students, or students about to start Linear Algebra who are looking to get ahead,Anyone who wants to study math for fun after being away from school for a while,Anyone who needs Linear Algebra as a prerequisite for Machine Learning, Deep Learning, Artificial Intelligence, Computer Programming, Computer Graphics and Animation, Data Analysis, etc.

Course Information:

Udemy | English | 9h 18m | 2.37 GB
Created by: Sm Bakhteyar

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