Data Splitting and Cross-Validation for ML

Building accurate machine learning models requires careful data preparation. A poorly split dataset leads to models that perform well on training data but fail completely in the real world. This course teaches you the foundational process of partitioning data correctly, preventing common errors like data leakage, and applying industry-standard validation techniques to reliably measure model performance. What you'll learn: * Understand the necessity of splitting data into distinct training, validation, and test sets. * Apply the standard train-test split function using common Python data science libraries. * Configure advanced splitting techniques like stratified splitting for handling imbalanced datasets. * Master k-fold cross-validation to maximize data utilization and reduce model variance. * Recognize and prevent the critical pitfalls of data leakage during the data preparation phase. * Practice measuring model performance metrics using only the designated test set. The course begins by defining core terminology and concepts, then progresses through practical, step-by-step written explanations demonstrating how to implement different splitting methodologies in a standard data science workflow. This course is designed for absolute beginners in machine learning and data science who need to establish a strong foundation in model evaluation techniques. No prior experience with statistical modeling is required. Start building models that generalize effectively to new, unseen data.