Blog Post about Machine Learning

Scikit-Learn is the workhorse of applied machine learning in Python. Here's what it does, why it's worth learning deeply, and a practical example to get you started.

Both entropy and Gini impurity measure how mixed a node is in a decision tree — but they differ in computation, behavior, and when each makes sense to use.

Both Lasso and Ridge add a penalty to linear models to prevent overfitting — but they behave very differently. Here's when to use each and how to implement both.

The Titanic dataset is the classic ML classification benchmark. Here's a full walkthrough — feature engineering, Random Forest, Logistic Regression, and what the results actually mean.

Grid Search exhaustively evaluates every combination in a parameter grid using cross-validation. Here's how it works, when to use it, and a complete implementation with Scikit-Learn.

When your parameter space is too large for Grid Search, Randomized Search samples combinations efficiently. Here's how it works and when it outperforms the exhaustive alternative.