Binary Classification and Decision Boundaries in PyTorch

Classification is at the heart of modern machine learning, but how do algorithms actually draw the line between different categories? Understanding decision boundaries is crucial for building, debugging, and optimizing predictive models. This text-based course guides you through the core mathematical and programming concepts behind binary classification, showing you how to construct, analyze, and implement decision boundaries using PyTorch. What you'll learn: - Understand the fundamental math behind binary classification and logistic regression. - Implement logistic regression models from scratch using modern PyTorch conventions. - Analyze how decision boundaries shift as model weights and biases change. - Evaluate the impact of high-dimensional feature spaces on class separability. - Write clean PyTorch code to compute loss, perform backpropagation, and update decision boundaries. - Interpret decision boundaries conceptually through structured code-based examples and mathematical intuition. You will start with foundational classification terminology and basic concepts before moving into practical PyTorch implementations. Through clear text explanations and step-by-step code walkthroughs, you will grasp how models learn to separate data. This course is designed for beginners in machine learning who want to build a strong conceptual foundation in classification. No prior experience with PyTorch is required, though a basic understanding of Python is helpful. Start mastering the foundational mechanics of machine learning classification today.