👉 The driving math behind optimization, a core concept in machine learning and data science, revolves around minimizing or maximizing a function, known as the objective function, to find the best possible solution for a given problem. This process involves defining a loss or cost function that quantifies how well the model's predictions match the actual data. Algorithms like gradient descent iteratively adjust the model parameters by moving in the direction that reduces this function's value, effectively seeking the local or global minimum (for minimization) or maximum (for maximization). This mathematical framework is crucial for tasks like regression, classification, and feature selection, enabling models to learn from data and make accurate predictions or decisions.
