Linear Algebra Full Course
Linear Algebra Full Course, available at $19.99, has an average rating of 3, with 5 lectures, based on 1 reviews, and has 568 subscribers.
You will learn about Learn about linear algebra will learn how to find eigen vectors and eigen values will be able to find determinants,transpose,inverse etc will be able to solve equations with help of matrices methods This course is ideal for individuals who are maths or students It is particularly useful for maths or students.
Enroll now: Linear Algebra Full Course
Summary
Title: Linear Algebra Full Course
Price: $19.99
Average Rating: 3
Number of Lectures: 5
Number of Published Lectures: 5
Number of Curriculum Items: 5
Number of Published Curriculum Objects: 5
Original Price: $39.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn about linear algebra
- will learn how to find eigen vectors and eigen values
- will be able to find determinants,transpose,inverse etc
- will be able to solve equations with help of matrices methods
Who Should Attend
- maths
- students
Target Audiences
- maths
- students
Linear Algebra
Linear algebra is a branch of mathematics that deals with linear equations and their representations in the vector space using matrices. In other words, linear algebra is the study of linear functions and vectors. It is one of the most central topics of mathematics. Most modern geometrical concepts are based on linear algebra.
Linear algebra facilitates the modeling of many natural phenomena and hence, is an integral part of engineering and physics. Linear equations, matrices and vector spaces are the most important components of this subject. In this article, we will learn more about linear algebra and the various associated topics.
What is Linear Algebra?
Linear algebra can be defined as a branch of mathematics that deals with the study of linear functions in vector spaces. When information related to linear functions is presented in an organized form then it results in a matrix. Thus, linear algebra is concerned with vector spaces, vectors, linear functions, the system of linear equations and matrices. These concepts are a prerequisite for sister topics such as geometry and functional analysis.
Linear Algebra Definition
The branch of mathematics that deals with vectors, matrices, finite or infinite dimensions as well as a linear mapping between such spaces is defined as linear algebra. It is used in both pure and applied mathematics along with different technical forms such as physics, engineering, natural sciences etc.
Branches of Linear Algebra
Linear algebra can be categorized into three branches depending upon the level of difficulty and the kind of topics that are encompassed within each. These are elementary, advanced and applied linear algebra. Each branch covers different aspects of matrices, vectors and linear functions.
Elementary Linear Algebra
Elementary linear algebra introduces students to the basics of linear algebra. This includes simple matrix operations, various computations that can be done on a system of linear equations and certain aspects of vectors.
Some important terms associated with elementary linear algebra are given below:
Scalars – A scalar is a quantity that only has magnitude and not direction. It is an element that is used to define a vector space. In linear algebra, scalars are usually real numbers.
Matrix – A matrix is a rectangular array wherein the information is organized in the form of rows and columns. Most linear algebra properties can be expressed in terms of a matrix.
Matrix Operations – These are simple arithmetic operations such as addition, subtraction and multiplication that can be conducted on matrices.
Advanced Linear Algebra
Once the basics of linear algebra have been introduced to students the focus shifts on more advanced concepts related to linear equations, vectors and matrices. Certain important terms that are used in advanced linear algebra are as follows:
Linear Transformations – The transformation of a function from one vector space to another by preserving the linear structure of each vector space.
Inverse of a Matrix – When an inverse of a matrix is multiplied with the given original matrix then the resultant will be the identity matrix. Thus, A-1A = I.
Eigenvector – An eigenvector is a non-zero vector that changes by a scalar factor (eigen value) when a linear transformation is applied to it.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: properties of vectors
Chapter 2: Eigen values and Eigen vectors
Lecture 1: properties
Lecture 2: introduction of eigen values
Lecture 3: eigen value example problem
Instructors
-
B Venkatesh
Aptitude and Math Trainer
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- 3 stars: 1 votes
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- 5 stars: 0 votes
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