Gradient Descent and Optimization in Python

Understanding how machine learning models learn requires looking under the hood at optimization algorithms. This comprehensive written course demystifies gradient descent, giving you the mathematical intuition and coding skills needed to optimize models from scratch. You will gain a solid grasp of objective functions, cost curves, and iterative convergence while implementing algorithms using clean Python and NumPy. What you'll learn: Understand foundational terminology, loss functions, and partial derivatives; Implement batch, stochastic, and mini-batch gradient descent from scratch; Apply gradient optimization to train linear and logistic regression models; Practice tuning hyperparameters such as learning rates and convergence thresholds; Explore modern optimization practices including vectorization with NumPy, type hints, and learning rate schedules. The course begins with essential theoretical definitions and core terminology before guiding you through structured code walkthroughs and practical written exercises. This guide is crafted for beginners in programming and data science who want to understand machine learning optimization step by step without complex prerequisites. Start reading today to master the core engine behind modern predictive modeling.