Don’t Worry About it (Part 1) : Linear Algebra

Welcome to the “Don’t Worry About It” series, where we break down the mathematical concepts that underpin modern Data Science and Machine Learning Algorithms. Even though we have powerful tools for creating machine learning systems, it’s crucial to grasp the basic math behind them. In this first chapter, we’ll dive into the world of Linear Algebra. Back when I started college, Linear Algebra used to be my stumbling block, and concepts like vectorial spaces and eigenvalues felt confusing. Little did I realize, these very concepts would lay the groundwork for the advanced algorithms that Data Scientists wield today. We represent numerical data as vectors and represent a table of such data as a matrix. Therefore, It’s crucial to understand the key principals of Linear Algebra.   Don’t worry if you’re not a math Guru, although this article assumes you have some familiarity with matrices, linear systems and concepts like the determinant and the rank of a matrice we are here to break them down and show you how these concepts are practically valuable. Let’s dive in <h1>Linear Algebra</h1> Linear algebra is all about working with these things called vectors. Vectors can be added together and stretched or shrunk (multiplied) to make new vectors. You might remember seeing “geometric vectors” in school, which are usually shown with a little arrow above a letter. These vectors aren’t just arrows; they’re objects that follow specific rules. When you add or stretch them, you always end up with another vector. But here’s the cool part: Things like R^n and even mathematical polynomials can behave like vectors too. <a href="https://medium.com/@haytham99cheikhrouhou/dont-worry-about-it-part-1-linear-algebra-d95e9dc59042">Website</a>