Companion code for Machine Learning From Scratch — 10 core ML algorithms built from scratch with NumPy, compared with Scikit-learn and PyTorch.

Academic implementation of a Multi-Layer Perceptron from scratch using Python and NumPy.

A hands-on implementation of Linear and Polynomial Regression from scratch using the real-world California Housing dataset. Includes comparisons of various optimization algorithms and professional libraries like Scikit-Learn and PyTorch.

End-to-end ML pipeline for California house price prediction. Features engineered data, OLS/Ridge/Lasso models, custom Gradient Descent, cross-validation, hyperparameter tuning, and a full training workflow.

🪐 Return players safely from the End to the Overworld when they fall or teleport, ensuring a smooth transition with simple, configurable options.