Data Preparation for Machine Learning in Python

Before you can train any machine learning model, you must ensure your data is clean, consistent, and correctly structured. Raw real-world data is almost always messy, containing missing values, outliers, and inconsistencies that can ruin your model's performance. This text-based course guides you through the foundational concepts and practical workflows of data preparation. You will progress from raw inputs to pristine, model-ready datasets, learning how to make informed decisions about imputation, scaling, and feature engineering along the way. What you'll learn: - Understand the core terminology of data quality, feature types, and prep pipelines. - Handle missing values and outliers using robust statistical imputation techniques. - Encode categorical variables and scale numerical features for optimal model performance. - Implement modern data validation patterns to catch structural errors early. - Construct reproducible data preparation pipelines that prevent data leakage. - Apply feature selection concepts to identify the most predictive variables. You will start with fundamental data cleaning definitions before moving step-by-step through practical Python implementations, focusing on writing clean, maintainable code for real-world datasets. This course is designed for aspiring data scientists, analysts, and developers who have a basic understanding of Python and want to master the critical first step of the machine learning workflow. No prior data science experience is required. Start reading today to build a solid foundation in data preparation and ensure your machine learning models deliver reliable, high-quality results.