Linear algebra is one of the most fundamental building blocks in data science and machine learning. Whether you’re working on recommendation systems, natural language processing, computer vision, or deep learning, linear algebra provides the tools to represent, transform, and understand data efficiently. This guide will walk you through the core concepts of linear algebra with simple explanations and practical relevance — helping you build a strong mathematical foundation for your data science journey.
1. Vectors and Their Role in Data Science
What is a Vector?
A vector is simply an ordered list of numbers. It’s one of the most fundamental concepts in linear algebra and is widely used in data science to represent:
- Features of a data point (such as age, income, height)
- Direction and magnitude in space (used in computer vision, recommendation systems, etc.)
Vector Examples
- A person’s data might be stored as a vector:
Example: [28, 175, 72] → (age: 28, height: 175 cm, weight: 72 kg)
- A movie rating vector from a user: [5, 3, 0, 4] → (ratings across 4 movies)
