Batch Gradient Descent and Perceptron Training with NumPy

Understanding the mathematical optimization behind machine learning is the key to building reliable models. This text-based course guides you through the core mechanics of batch gradient descent, the foundational optimization algorithm used to train neural networks. You will transition from theoretical math to practical Python code, learning how to update weights and biases to solve linear classification problems. By implementing these concepts from scratch, you will develop a deep, intuitive grasp of how models actually learn. What you'll learn: Understand the fundamental terminology of perceptrons, weights, biases, and decision boundaries; Implement batch gradient descent to update model parameters systematically; Write clean, vectorized NumPy code for linear classification tasks; Apply mathematical derivatives to compute gradients for loss functions; Compare batch updates with basic stochastic approaches to understand training efficiency; Structure your machine learning code using modern Python practices like type hints. The course begins with foundational definitions of linear classifiers and perceptrons. You will then progress step-by-step through the math of gradient descent, translating those equations into structured NumPy code through clear, written explanations and code exercises. This course is designed for beginner programmers and aspiring data scientists who want to understand the math behind machine learning without relying on high-level libraries. Basic familiarity with Python is recommended, but no prior machine learning experience is required. Start reading today to master the core optimization mechanics of machine learning from the ground up.