Feature Engineering for Machine Learning in Python

The Korshub Take

This intermediate Python course focuses on the critical, often-overlooked stage of machine learning: data preparation and feature engineering. Working with datasets like the Stack Overflow Developer Survey and historic US presidential speeches, you will learn to transform raw continuous, categorical, and unstructured data into model-ready features.

Who It's For

Data scientists and ML engineers who want to systematically extract better features from real-world datasets to boost predictive model performance.

Key Takeaways

• Categorical & Continuous Engineering: Encode categorical variables, handle missing data, and apply scaling and binning techniques.
• Text & Unstructured Data: Extract numerical features from unstructured text data for machine learning algorithms.
• Real-World Datasets: Practice feature engineering workflows using Stack Overflow survey data and historical inauguration speeches.