Handwritten Digit Classification with SVM and Logistic Regression

Image classification is a cornerstone of modern artificial intelligence, and the MNIST dataset is the perfect starting point for mastering it. Understanding how to handle pixel data and apply classic machine learning algorithms prepares you for complex computer vision challenges. In this text-only course, you will transition from a beginner to confidently building and optimizing classification models. You will learn how to preprocess raw pixel data, implement Support Vector Machines (SVM) and Logistic Regression, and apply systematic hyperparameter tuning to maximize model accuracy. What you'll learn: - Understand the foundational structure of image datasets and pixel representations. - Preprocess and normalize image data for optimal machine learning model performance. - Implement Logistic Regression for multi-class classification tasks. - Configure and train Support Vector Machines (SVM) with various kernel functions. - Tune hyperparameters systematically using modern validation techniques to prevent overfitting. - Evaluate model performance using precision, recall, F1-score, and confusion matrices. The course begins with essential terminology and data exploration, guiding you step-by-step through preprocessing, model implementation, evaluation, and optimization techniques. You will practice through clear, written explanations and structured coding exercises. This course is designed for aspiring data scientists, machine learning beginners, and programmers looking to understand the fundamentals of image classification without needing advanced prerequisites. Start reading today to build your first image classification models from scratch.