Data Preparation for Machine Learning in Python

Raw data is almost never ready for machine learning algorithms. To build models that deliver precise, reliable predictions, you must first master the art of data cleaning and feature engineering. This text-based course guides you from raw, messy datasets to clean, optimized inputs ready for machine learning pipelines. You will gain the practical skills needed to handle real-world data challenges confidently using Python and its standard data libraries. What you'll learn: 1. Understand foundational data preparation concepts, terminology, and the preprocessing pipeline. 2. Clean messy datasets by handling missing values, duplicates, and outliers using modern pandas conventions. 3. Transform categorical variables and scale numerical features for optimal model performance. 4. Engineer new features to expose hidden patterns and improve predictive power. 5. Split datasets correctly to prevent data leakage and ensure robust model evaluation. 6. Practice writing clean, readable Python code with modern type hints for data pipelines. You will start with core terminology and fundamental concepts of data quality before moving step-by-step through practical data cleaning, transformation, and feature engineering techniques. Through written explanations and realistic code snippets, you will build a solid workflow for preparing high-quality data. This course is designed for beginners in data science and machine learning who have a basic familiarity with Python. No prior data preprocessing experience is required. Start mastering the crucial first step of the machine learning workflow today.