Building Baseline Models for Machine Learning

To know if your machine learning model is actually performing well, you must first establish a clear point of comparison. Without a baseline model, impressive accuracy metrics can be highly misleading. This text-based course guides you through the essential process of creating simple, reliable benchmarks to evaluate your predictive algorithms effectively.\n\nYou will transition from guessing about your model's performance to confidently validating its real-world utility using structured baseline strategies.\n\nWhat you'll learn:\n- Understand the core concepts of machine learning workflows, data splitting, and validation strategies.\n- Define simple baseline models using heuristic rules and dummy estimators to set performance benchmarks.\n- Split dataset structures into training and testing sets while avoiding common data leakage pitfalls.\n- Compare complex model metrics against your baseline to justify the need for advanced algorithms.\n- Apply modern validation practices, including stratified splits and basic evaluation pipelines.\n- Identify and avoid common evaluation traps like class imbalance bias and overfitting.\n\nThis course begins with key terminology, basic concepts, and foundational definitions of model evaluation before guiding you through written exercises to construct and analyze your first baseline models. Designed specifically for beginners, this course requires no prior machine learning experience. Start establishing reliable benchmarks for your machine learning projects today.