From Vectors to Matrices: A Comprehensive Introduction to Linear Algebra

What you will learn

People looking to pursue a career in Data Science

High School or College Students

Aspiring Machine Learning Engineers

Lifelong learners

Description

Introduction to Linear Algebra is a foundational course designed to provide students with a solid understanding of the fundamental concepts and techniques of linear algebra. Throughout the course, students will explore vectors and matrix operations, systems of linear equations, eigenvalues/vectors, diagonalization, linear transformations, bases, and subspaces, which are all key components of this important mathematical field.

Students will begin by studying the basic properties of vectors and matrices, including how to perform vector addition, scalar multiplication, and matrix operations. They will also learn how to solve systems of linear equations, both algebraically and graphically.


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Moving on, students will delve into the concept of eigenvalues and eigenvectors, exploring how they relate to linear transformations and diagonalization. They will also investigate subspaces, bases, and span briefly in order to gain an awareness of the more abstract side of Linear Algebra.

Throughout the course, students will have the opportunity to develop their problem-solving skills through a variety of quiz questions. By the end of the course, students will have a strong foundation in linear algebra that will prepare them for further study in mathematics, engineering, computer science, and other fields that rely on this important subject. No matter whether you are an aspiring data scientist or you are already well into your career, this course can act as both a launching pad and a refresher on key concepts in linear algebra.

English
language

Content

Vectors & Matrices

Adding & Scaling Vectors
Calculating Vector Lengths & Normalizing Vectors
Dot Products & Matrix Multiplication
Determinant & Inverse of 2×2 Matrix
Vectors & Matrix Operations

Systems of Linear Equations

What Is A System of Linear Equations?
Solving System of Linear Equations – Gaussian Elimination
Cramer’s Rule
Solving Systems of Linear Equations

Eigenvalues & Eigenvectors

Eigenvalues of 2×2 Matrix
Eigenvectors of 2×2 Matrix
Diagonalization
Eigenvalues, Eigenvectors, and Diagonalization

Linear Transformations & Vector Spaces

What Is A Linear Transformation?
Linear Transformations as Matrix Vector Products
Subspaces, Basis, and Span
Linear Transformations & Vector Spaces