Logistic Regression for Binary Classification on MNIST

Classification is a cornerstone of machine learning, yet moving from theory to practice can feel daunting. This text-based course guides you step-by-step through building a binary classifier to recognize handwritten digits, giving you a solid foundation in predictive modeling. By studying the written explanations and practical code examples, you will transform raw data into a trained machine learning model. You will learn not just how to run code, but how to think like a data scientist—from initial data preparation to rigorous model evaluation. What you'll learn: * Understand the mathematical foundations of logistic regression and binary classification * Prepare and preprocess the MNIST handwritten digit dataset for binary tasks * Build structured machine learning pipelines using modern scikit-learn conventions * Evaluate model performance using precision, recall, F1-score, and ROC-AUC curves * Tune hyperparameters systematically to improve classification accuracy * Apply clean coding practices, including type hints, to your machine learning scripts You will start with key machine learning terminology, basic mathematical concepts, and foundational definitions before diving into hands-on code. The course then walks you through data loading, preprocessing, model training, and modern evaluation techniques using clear, written explanations. This course is designed for beginner programmers and aspiring data scientists who want a practical, clear introduction to machine learning without complex prerequisites. Start reading today to build your first image classification model from scratch.