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Regression analysis that models relationships as nth-degree polynomials.
Distinguishing note: Distinct from linear regression by allowing non-linear curve fitting.
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This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models. The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base
Models non-linear relationships between variables by fitting polynomial functions to data.