Linear Algebra for Machine Learning

Linear algebra is the hidden engine that drives modern artificial intelligence and machine learning. Every concept from training a simple regression model to building massive neural networks is built on the foundation of vectors, matrices, and transformations. Yet for many learners, linear algebra feels abstract and disconnected from real-world use.

This course will teach you linear algebra not as dry mathematics, but as the language of machine learning. We begin with the essentials. Scalars, vectors, and matrices, and gradually uncover their deeper meaning through geometry and transformations. You will learn how dot products measure similarity, how eigenvalues and eigenvectors reveal structure, and how decompositions like SVD power dimensionality reduction methods such as Principal Component Analysis (PCA).

Along the way, every topic is tied back to machine learning applications. You will see how matrix multiplication underlies neural networks, how linear systems explain regression, and how optimization depends on vector calculus. By the end of the course, you will not only understand the mechanics of linear algebra but also gain the intuition to connect it to real AI systems.

This course is designed for complete beginners, professionals transitioning into AI, and researchers who want to strengthen their mathematical foundations. With self-paced lectures, intuitive explanations, numerical examples, quizzes, and assignments, you will develop the confidence to use linear algebra as a tool rather than a hurdle.