Data Preprocessing and Normalization for Machine Learning Pipelines

Raw data is rarely ready for machine learning, and poor preprocessing is a leading cause of model failure in production. Building a successful model starts with mastering data quality, parsing, and normalization. This text-based course guides you from raw, messy datasets to clean, model-ready pipelines. You will understand how to handle missing values, scale features, and parse complex formats systematically. What you'll learn: - Learn the fundamentals of data quality and structure for machine learning - Parse unstructured and semi-structured data formats into clean dataframes - Apply scaling, normalization, and encoding techniques to prepare features - Handle missing data and outliers using robust statistical strategies - Validate data quality and schemas using modern validation practices - Build reproducible preprocessing pipelines using Python libraries You will start with foundational data quality concepts and terminology before moving into hands-on parsing techniques. As you progress, you will read through practical examples of feature engineering, normalization, and structured pipeline design. This course is designed for beginners and aspiring data scientists with no prior data engineering experience required. Start reading today to build reliable, high-performance machine learning pipelines.