Gradient Descent is one of the most important algorithms in Machine Learning and Deep Learning. Every modern AI model—from linear regression to neural networks — uses optimization techniques based on Gradient Descent to learn from data. This course is designed to help you understand these concepts from the ground up through intuitive explanations, visualizations, and hands-on Python examples.
You’ll begin by learning the fundamentals of optimization, loss functions, gradients, and learning rates before exploring how Gradient Descent minimizes error step by step. The course then introduces optimization landscapes, local and global minima, saddle points, and convergence, helping you develop a strong conceptual understanding of how machine learning models improve during training.
You’ll implement Gradient Descent from scratch in Python, compare Batch, Stochastic, and Mini-Batch Gradient Descent, and explore advanced optimization algorithms such as Momentum, RMSProp, Adam, and learning rate scheduling. Throughout the course, practical demonstrations and quizzes reinforce every concept.
