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The Essentials of Linear Algebra for Machine Learning for Beginners

Avi Arora
Machine Learning | Computer Vision | Python | React.js | Data Science Writer
・1 min read

Read the Full Article: https://analyticsarora.com/linear-algebra-foundations-of-machine-learning/

Introduction

Linear algebra is the backbone of machine learning and is critical for learners to have a solid understanding of the concepts before jumping into the core of machine learning. First, let’s take some time to understand why linear algebra before getting into the crux of linear algebra.

Article Overview

  • Why Linear Algebra?
  • What is the Transpose of a Matrix?
  • Different Forms of Matrices
  • Special Matrices
  • Norms of Matrices
    • What Types of Norms are Used in Machine Learning?
  • Multiplications of Matrices and Vectors
    • Matrix Multiplication
    • Vector Multiplication
  • Linear Independence and the Rank of a Matrix
    • Examples
  • How to do Matrix Inversion?
  • Trace and Determinant of a Matrix
    • How to find the Determinant of a 2 x 2 Matrix?
    • What are the Properties of a Determinant?
  • Eigenvalues and Eigenvectors
    • Steps to Calculating the Eigenvalues and Eigenvectors
    • Example
  • What is Singular Value Decomposition (SVD)?
    • Singular Value Decomposition for Dimensionality Reduction

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