Master linear algebra: theory and implementation in code

You need to learn linear algebra. Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on. You need to know applied linear algebra, not just abstract linear algebra.

The way linear algebra is presented in 30-year-old textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications. The answer may surprise you, and it's in this course.

If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e. g. , statistics or signal processing), then this course is for you.

You'll see all the maths concepts implemented in MATLAB and in Python. Unique aspects of this courseClear and comprehensible explanations of concepts and theories in linear algebra. Several distinct explanations of the same ideas, which is a proven technique for learning.