26 Important Concepts that you should learn in Linear Algebra for Machine Learning — PART I

Last Updated on August 24, 2026 by Editorial Team Author(s): Rajendran S Originally published on Towards AI. Machine Learning is the most fascinating field in the era of AI development. While many people put effort into learning machine learning, most of them fail to understand the core mathematics behind predictive models. You don’t need to learn very advanced math like PhD students do. You need to know very few fundamentals to understand things better in the world of machine learning. Especially, you will have to learn the basics in four domains: Linear Algebra Calculus Probability Statistics Once you master these, you can break down any complex derivation that you see in the machine learning papers. Moreover, it helps to understand how traditional ML algorithms work. This blog dives into the most important concepts that you will have to know in linear algebra. This is a two-part blog, which has 13 concepts each. Each concept will be explained in terms of intuition, layman's explanation, formal and mathematical definition, and its application in machine learning. What is Linear Algebra? Linear algebra is the branch of mathematics that deals with vectors, matrices, and linear tra…