Udemy – Math 0-1: Linear Algebra for Data Science & Machine Learning 2023-8

Udemy – Math 0-1: Linear Algebra for Data Science & Machine Learning 2023-8 Downloadly IRSpace

Apr 17, 2025 - 12:04 Updated: Apr 24, 2025 - 15:11

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$Udemy – Math 0-1: Linear Algebra for Data Science & Machine Learning 2023-8$

Udemy – Math 0-1: Linear Algebra for Data Science & Machine Learning 2023-8

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. 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. We will 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, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.

What you’ll learn

Solve systems of linear equations
Understand vectors, matrices, and higher-dimensional tensors
Understand dot products, inner products, outer products, matrix multiplication
Apply linear algebra in Python
Understand matrix inverse, transpose, determinant, trace
Understand matrix rank and low-rank approximations (e.g. SVD)
Understand eigenvalues and eigenvectors

Who this course is for

Anyone who wants to learn linear algebra quickly
Students and professionals interested in machine learning and data science but who’ve gotten stuck on the math