Math 0-1: Linear Algebra for Data Science & Machine Learning

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH. Either you never studied this math, or you studied it so long ago you've forgotten it all. What do you do.

Well my friends, that is why I created this course. Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.

The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course. If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra. In a normal STEM college program, linear algebra is split into multiple semester-long courses.

Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters. This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI art) and LLMs (Large Language Models) using LoRA.