Math for Machine Learning : Linear Algebra

Understand fundamental concepts of vectors and matrices. Apply matrix operations to solve systems of linear equations. Analyze transformations and their properties using linear algebra. Utilize eigenvalues and eigenvectors for various applications. Apply linear algebra techniques to real-world problems in various fields.

Instructors:

SJ Sushmitha J

Start Learning

1 Enrolled

55 Lessons

This course provides a comprehensive introduction to linear algebra, covering topics such as vectors, matrices, systems of linear equations, transformations, eigenvalues, and eigenvectors. Through a combination of theoretical understanding and practical applications, you will develop the skills necessary to solve complex problems.

Course Content

Introduction

Intro

Note

Math for Machine Learning : Linear Algebra Module 1

Introduction to Linear Algebra

Solving Linear Systems, Part One

Solving Linear Systems, Part Two

Describing Solution Sets, Part One

General = Particular + Homogeneous

Vectors in Space

Vector Length and Angle Measure

Gauss-Jordan Elimination

The Linear Combination Lemma

Math for Machine Learning : Linear Algebra Module 2

Vector Spaces, Part One

Vector Spaces, Part Two

Subspaces, Part One

Subspaces, Part Two

Linear Independence, Part One

Linear Independence, Part Two

Basis, Part One

Basis, Part Two

Dimension

Vector Spaces and Linear Systems

Math for Machine Learning : Linear Algebra Module 3

Isomorphism, Part One

Isomorphism, Part Two

Dimension Characterizes Isomorphism

Homomorphism, Part One

Homomorphism, Part Two

Range Space and Null Space, Part One

Range Space and Null Space, Part Two

Extra Transformations of the Plane

Representing Linear Maps, Part One

Representing Linear Maps, Part Two

Any Matrix Represents a Linear Map

Sums and Scalar Products of Matrices

Matrix Multiplication, Part One

Matrix Multiplication, Part Two

Mechanics of Matrix Multiplication

Matrix Inverse, Part One

Matrix Inverse, Part Two

Changing Vector Representations

Changing Map Representations, Part One

Changing Map Representations, Part Two

Projection

Math for Machine Learning : Linear Algebra Module 4

Determinants

Permutation Expansion, Part One

Permutation Expansion, Part Two

Determinants Exist (optional)

Geometry of Determinants

Laplace's formula for the determinant

Math for Machine Learning : Linear Algebra Module 5

Complex Vector Spaces

Similarity

Diagonalizability

Eigenvalues and Eigenvectors, Part One

Eigenvalues and Eigenvectors, Part Two

Geometry of Eigenvalues and Eigenvectors

Assessment on Linear Algebra

Reviews

Review the course

Help us improve our course material.

Write a review