Classification with Regression Models in Python

How do data scientists train computers to categorize data, predict outcomes, and make decisions? Understanding how classification problems can be solved using regression models is a foundational step in mastering modern machine learning. In this text-based course, you will learn the core concepts that bridge regression and classification. You will understand how to transform raw data, configure model outputs, and evaluate predictions using industry-standard Python libraries and clean, modern code practices. What you'll learn: - Understand the fundamental differences and connections between regression and classification models. - Apply one-hot encoding to prepare categorical data for machine learning algorithms. - Implement the softmax function to convert raw regression outputs into class probabilities. - Use categorical cross-entropy loss to evaluate and guide model training. - Build and evaluate basic classification models using modern Python libraries and type hints. - Interpret model metrics to assess performance and make data-driven improvements. The course begins with essential definitions and the mathematical intuition behind classification. You will then progress through step-by-step written explanations and code exercises, learning how to implement encoding, activation functions, and loss calculations. This course is designed for beginner data analysts, aspiring data scientists, and developers who want a solid conceptual and practical foundation in machine learning. No prior machine learning experience is required, though basic Python familiarity is helpful. Start building your data science toolkit today by mastering classification and regression fundamentals.