This page collects my projects, notes, and implementations for learning and reviewing the linear algebra foundations behind modern machine learning and data science.

Linear algebra plays a central role in topics such as optimization, regression, dimensionality reduction, neural networks, and scientific computing. Through this collection, I aim to build both conceptual understanding and practical implementation skills.

What this collection covers

This collection focuses on:

• systems of linear equations,
• matrix factorizations,
• vector spaces and subspaces,
• eigenvalues and eigenvectors,
• numerical linear algebra for data science,
• computational implementations in Python.

Featured topic

Methods of Solving a System of Linear Equations

Systems of linear equations appear throughout science, engineering, economics, and computer science. In this project, I explore how matrix algebra and optimization techniques can be used to solve such systems effectively.

The goal is not only to present the mathematical theory, but also to connect it with practical computation and algorithm design.

What you will find in this project

• the mathematical formulation of linear systems,
• geometric intuition behind solution methods,
• exact and iterative solution techniques,
• implementation details in code,
• examples relevant to machine learning and data science.

Planned additions

This collection will continue to expand with notes and projects on topics such as:

• matrix decompositions,
• least squares methods,
• orthogonality and projections,
• singular value decomposition,
• eigendecomposition,
• applications in machine learning pipelines.

Why this page exists

I created this page as both a personal learning archive and a public-facing reference for anyone interested in strengthening their foundations in linear algebra for machine learning and data science.