Solving Linear Systems with Numerical Methods in Python

Systems of linear algebraic equations are central to scientific computing, quantitative modeling, and data science. This course provides a clear, beginner-friendly introduction to numerical linear algebra, guiding you through both foundational theory and code implementation. You will learn key mathematical definitions and matrix properties before exploring practical algorithm design. Understand core direct solvers such as Gaussian elimination and LU matrix decomposition, as well as iterative approaches including Jacobi and Gauss-Seidel techniques. You will also learn modern Python practices, leveraging clean code structures and vectorized NumPy operations to write efficient computational routines. Analyze numerical stability, floating-point error propagation, and matrix conditioning through clear written explanations and step-by-step code analysis. The course begins with essential mathematical terminology and matrix concepts before systematically progressing to complete algorithm implementations. Designed for beginners in computational mathematics and programming, this course requires no prior background in numerical analysis. Start reading today to build a strong foundation in Python and numerical problem solving.