Online Algebra for Data Science & Machine Learning 2023-8 – Download

Explanation

Math 0-1: Straightforward Algebra for Data Science & Machine Learning, General Situation: You’re trying to get into machine learning and data science, but there’s too much math. Either you never learned this math, or you learned it long ago and forgot all about it. Linear algebra is one of the most important mathematical terms in machine learning. There is a need to understand probability and statistics, which are fundamental to data science. “Data” in data science is represented using matrices and vectors, which are the central object of study in this course.

If you want to do machine learning beyond copying library code from blogs and tutorials, you should know linear algebra. In a typical college STEM program, linear algebra is divided into several semester-long courses. Fortunately, I have refined this teaching with the essentials, so that you can learn everything you need to know in an hour instead of a semester. The course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), lower order approximations, positive and negative definitions, and values and eigenvectors. It will even include machine learning that you won’t normally see in a typical college course, such as how these concepts apply to GPT-4, and the optimization of modern neural networks such as diffusion models (in the art of artificial intelligence) and LLMs. We’ll even demonstrate many of the concepts in this course using the Python programming language (don’t worry, you don’t need to know Python for this course). In other words, this course takes the most useful and impactful topics, rather than the dry college version of straight algebra, and gives you skills that are directly applicable to machine learning and data science, so that you can today can start applying.