📄️ Missing Data
Techniques for identifying, analyzing, and resolving missing values in datasets using deletion and imputation strategies.
📄️ Feature Engineering
A comprehensive guide to creating, transforming, and selecting features to maximize Machine Learning model performance.
📄️ Feature Scaling
Mastering the techniques used to harmonize feature scales, ensuring faster convergence and better model accuracy.
📄️ Normalization
A deep dive into Min-Max scaling, MaxAbs scaling, and Unit Vector normalization for bounded data ranges.
📄️ Dimensionality Reduction
Reducing feature complexity while preserving information: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
📄️ Feature Selection
Techniques for identifying and keeping only the most relevant features using filter, wrapper, and embedded methods.