Preparing Categorical Data for Machine Learning

Raw real-world data is rarely ready for machine learning algorithms, as most models require numerical inputs. This text-based course teaches you how to identify, analyze, and convert categorical and boolean features into optimized formats for machine learning. What you'll learn: - Understand the core differences between nominal, ordinal, and binary categorical features. - Apply essential encoding techniques including one-hot encoding, label encoding, and target encoding. - Analyze high-cardinality features and implement strategies to reduce dimensionality. - Handle missing categorical values using modern imputation methods. - Configure preprocessing pipelines using pandas and TensorFlow to prepare data for model training. - Practice feature engineering workflows through clear, step-by-step written tutorials and code snippets. You will start with foundational data type concepts before progressing to hands-on encoding techniques and building robust input pipelines. This course is designed for aspiring data scientists and machine learning beginners; basic Python familiarity is recommended but no prior preprocessing experience is needed. Start mastering the art of feature engineering today.