Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize.
Source: Principal Component Analysis explained visually, an article by Victor Powell with text by Lewis Lehe.
Calibrated models make probabilistic predictions that match real world probabilities. This post explains why calibration matters, and how to achieve it. It discusses practical issues that calibrated predictions solve and presents a flexible framework to calibrate any classifier. Calibration applies in many applications, and hence the practicing data scientist must understand this useful tool.
Source: Why model calibration matters and how to achieve it, an article by Lee Richardson & Taylor Pospisil
One of the ideas of functional programming is having pure functions, functions that have no side effects. But writing programs made exclusively from functions without side effects can't be useful in the real world, because programs have to affect the real world somehow. Inevitably, this means some parts of our programs must be effectful for the program to be useful.
Source: Patterns of Functional Programming: Functional Core - Imperative Shell, an article by Javier Casas.