A tutorial explaining the internals of Principal Component Analysis (PCA) for dimensionality reduction in machine learning and data analysis.
A guide to feature scaling and normalization in machine learning, covering standardization, Min-Max scaling, and their implementation in scikit-learn.
Explains feature scaling and normalization in machine learning, comparing standardization and Min-Max scaling, with examples using scikit-learn.
A technical guide to implementing Principal Component Analysis (PCA) for dimensionality reduction, comparing it with MDA and providing code examples.
An author critiques the overuse of PCA in data science, arguing it's not a universal solution for classification problems.
