PyTorch Classification: Building Logistic Regression Models

Want to transition from basic regression to predicting categories and classes? Classification is a cornerstone of machine learning, and PyTorch provides the flexibility to build these models from scratch. In this text-based course, you will journey from the mathematical foundations of logistic regression to writing clean, production-ready PyTorch code. You will understand exactly how raw outputs transform into probabilities and how to evaluate your model's performance with confidence. What you'll learn: - Understand the foundational concepts of binary classification, logits, and the sigmoid activation function. - Build custom classification models using PyTorch's neural network modules. - Implement binary cross-entropy loss functions and utilize optimization algorithms to train models. - Evaluate model performance using key metrics such as accuracy, precision, and recall. - Structure clean PyTorch data pipelines, including data normalization and train-test splits. The course begins with essential terminology, clarifying the differences between regression and classification. You will then progress through step-by-step written explanations and code snippets, exploring tensor operations, model definition, training loops, and evaluation metrics. This course is designed for beginners in machine learning who want to learn PyTorch. No prior deep learning experience is required, though a basic familiarity with Python is recommended. Start reading today to build your first PyTorch classification model.