Building a comprehensive library of core ML algorithms from mathematical primitives using only NumPy — covering regression (Linear Regression, Regularization, Logistic Regression, Auto Regression), tree-based methods (Decision Trees, Random Forests, Gradient Boosted Trees), unsupervised learning (PCA, K-Means, DBSCAN), and neural networks (MLP with backpropagation) — no high-level ML frameworks used.
| Model |
|---|
| Linear Regression |
| Regularization |