Essence of linear algebra, Chapter 1
Unknown
Vectors and intuition for applications
More resources on Applications of Linear Algebra
Mathematics for Machine Learning: Linear Algebra
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.
3Blue1Brown Linear Algebra Series
Visualizations for intuitive applications
Mathematics for Machine Learning
Free PDF book with LA applications to ML
Gilbert Strang: Applications of Linear Algebra
Real-world uses from MIT
Linear Algebra: Foundations to Frontiers Lecture 1
Intro to modern applications
Linear Algebra
Learn linear algebra and its real-world applications with Gilbert Strang's MIT course. Master concepts and problem-solving!