Data Quality

This lecture explores the crucial aspects of data quality in machine learning. We will cover data cleaning, preprocessing, and transformation techniques essential for preparing datasets for various ML algorithms. The lecture emphasizes practical approaches to handle missing values, outliers, data normalization, feature engineering, and data validation to ensure high-quality input for ML models.