Multiclass Classification and Imbalanced Classes in Python

Real-world data is rarely perfectly balanced, and classification often goes beyond simple binary choices. Understanding how to handle multiple classes and highly skewed datasets is a critical skill for any aspiring data scientist. This text-only course guides you through the core concepts of multiclass classification and imbalanced datasets, teaching you how to prepare data, select the right algorithms, and evaluate models accurately. What you'll learn: - Understand the fundamental differences between binary and multiclass classification. - Implement strategies like One-vs-Rest (OvR) and One-vs-One (OvO) for multi-category problems. - Address class imbalance using modern resampling techniques and synthetic data generation. - Apply cost-sensitive learning by configuring class weights in machine learning models. - Evaluate model performance using robust metrics like balanced accuracy, macro/micro F1-score, and confusion matrices. - Avoid common pitfalls such as data leakage during the preprocessing and resampling phases. You will start with essential terminology and theoretical foundations before exploring step-by-step written explanations and code implementations. This course is designed for beginners who have a basic understanding of Python and want to level up their data science skills. Start reading today to build more robust and reliable classification models.