Advanced Data Wrangling and Cleaning
Dive deep into advanced techniques for data wrangling and cleaning using pandas and NumPy, including handling complex data structures, advanced imputation, dealing with outliers, and automating data cleaning processes.
Data Splitting Techniques
Learn the theory and practice of data splitting, including train/test/validation splits, their importance in model evaluation, and best practices for ensuring representative and non-leaky splits using pandas and NumPy.
Introduction to Feature Engineering
Dive deep into feature engineering—understand what it is, why it's crucial, and explore various techniques to enhance the predictive power of machine learning models, complete with visual examples and code snippets.
When Do I Need What Features?
Learn how to decide which features to engineer based on the characteristics of your data and the requirements of your machine learning model.
Applied Feature Engineering
Learn the techniques of feature engineering, including creating new features, transforming existing features, and encoding categorical variables using pandas and NumPy.