What you'll learn

Machine learning models can only learn from the data they're given, making feature engineering one of the most important steps in building effective predictive models. In this course, Getting Started with Feature Engineering, you'll learn how to transform raw data into meaningful features that improve machine learning performance. First, you'll explore the role of feature engineering and how it fits into the machine learning workflow. Next, you'll discover common techniques for transforming, encoding, scaling, and deriving features while applying domain knowledge to create stronger learning signals. Finally, you'll build and evaluate engineered features using Python, pandas, and scikit-learn to see how better inputs can improve model performance. When you're finished with this course, you'll have the skills and knowledge of feature engineering needed to prepare data, design effective features, and build more accurate machine learning models.