Foundations of Gradient Descent for Machine Learning

Understanding how modern machine learning models learn requires a firm grasp of optimization algorithms. Gradient descent is the foundational engine behind training neural networks and statistical models. This written course guides you through the fundamental mathematics of differentiation, finding function extrema, and building iterative optimization routines from scratch. You will start with core mathematical concepts and terminology before exploring practical code snippets that demonstrate step-by-step optimization. What you will learn: Understand fundamental differentiation concepts and single-variable functions; Master the iterative mechanics of single-variable gradient descent algorithms; Implement gradient descent optimization routines using clear code snippets; Analyze learning rates, step sizes, and convergence behavior; Explore core principles of loss functions and modern optimization extensions. The content progresses sequentially from standard single-variable calculus principles to iterative algorithmic updates, preparing you for multi-dimensional machine learning challenges. This course is ideal for beginner data scientists, programmers, and math enthusiasts seeking a solid foundation in optimization with no prerequisites required. Start reading today to unlock the mathematical engine behind machine learning.