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Linear Algebra: Linear Transformations & Vector Spaces
Rating: 4.4 out of 5(11 ratings)
67 students

Linear Algebra: Linear Transformations & Vector Spaces

Exploring the foundations of linear algebra through the study of linear transformations and vector spaces"
Created byTensor Teach
Last updated 2/2023
English

What you'll learn

  • Learn what linear transformations are and how to define them.
  • Learn what a basis is and how to change basis.
  • Learn about span, row space, column space, null space and how these concepts relate to linear transformations.
  • Learn what eigenvalues & eigenvectors are and how to derive them.
  • Learn what an abstract vector space is.

Course content

5 sections52 lectures5h 29m total length
  • What Is A Linear Transformation?6:15
  • Projections As Linear Transformations5:46
  • Proof That A Projection Is A Linear Transformation8:23
  • Linear Transformations: Rotations6:29
  • Revisiting Rotations4:26
  • Linear Transformations: A Non-Example5:35
  • Defining & Identifying Linear Transformations
  • Linear Transformations As Matrix-Vector Product4:07
  • Constructing A Projection Matrix8:00
  • Projections & Transformations
  • What Is A Basis?8:50
  • Linear Transformations: Transforming Basis Vectors6:58
  • Building A Linear Transformation Matrix7:12
  • Determinants As Linear Transformations9:47
  • When The Determinant of A Transformation Matrix is 09:29
  • Bases, Linear Transformations & Determinants

Requirements

  • Matrix Algebra Fundamentals

Description

In this course, you will learn about some important concepts in math called linear transformations and vector spaces. These concepts are used to understand how to work with shapes and patterns in math.

We will learn about matrices, which are like grids of numbers that can be used to represent linear transformations. We will also learn about vectors, which are like arrows that can be added and subtracted to find new positions.

One of the main things we will learn about is called a basis, which is a set of vectors that can be used to represent any other vector in a vector space. We will also learn about something called the Gram-Schmidt process, which is a way to turn a set of vectors into an "orthonormal" basis, which means that the vectors are all perpendicular to each other and have a length of 1.

Throughout the course, we will practice using these concepts and techniques to solve problems, such as finding Transformation matrices, transforming vectors, and solving systems of linear equations.

This course is a good opportunity to learn more about math and how it can be used to understand patterns and shapes in the world around us. This course is for you if you are looking to pursue a career in a mathematical field such as Data Science, you're a student, or you are just looking to further your mathematics education.

Who this course is for:

  • This course is intended for anyone that is looking to take their Linear Algebra knowledge & understanding to the next level.
  • This course is intended for anyone looking to pursue a career in Data Science.