Numerical Root Finding in Python and MATLAB

Many real-world mathematical equations cannot be solved using traditional algebraic methods. Numerical root finding provides the essential computational tools to approximate these solutions with high precision. This text-based course guides you from mathematical theory to practical code implementation, helping you transition from understanding basic convergence to writing clean, optimized algorithms in both Python and MATLAB. What you'll learn: - Understand the foundational theory of convergence, error tolerances, and interval bracketing. - Implement classic bracketing algorithms including the Bisection and Regula Falsi methods. - Develop open methods such as the Newton-Raphson and Secant algorithms for rapid convergence. - Write clean, type-hinted Python code using modern NumPy vectorization for mathematical operations. - Apply native MATLAB functions and write custom scripts to compare algorithm performance and numerical stability. - Analyze common failure modes, chaotic behavior, and sensitivity to initial guesses in iterative solvers. You will begin by learning core mathematical definitions and the concept of iterative approximation. From there, you will progress through written step-by-step code implementations, comparing how Python and MATLAB handle numerical precision and algorithmic efficiency. This course is designed for students, engineering professionals, and beginning programmers who want to bridge the gap between mathematics and computer science. No advanced programming background is required, as we build all concepts from the ground up. Start coding your own mathematical solvers today.