I implemented 10 core ML algorithms from scratch with NumPy. Here's what no tutorial taught me [P]

In three linesImplementation of 10 classical ML algorithms (regression, KNN, decision trees, XGBoost, neural networks) in pure NumPy, validated against Scikit-learn and PyTorch. Open-source repo with Jupyter notebooks runnable locally or on Colab. Author emphasizes modular structure importance and gradient descent understanding.

Summary generated by Claude — human-verified