rushter/MLAlgorithms
MLAlgorithms
MLAlgorithms is an educational machine learning algorithm library consisting of core predictive models implemented from scratch in Python. It serves as a reference for developers to study the internal logic and mathematical workings of these models through clean, minimal implementations.
The codebase focuses on the study of algorithm implementation and machine learning education, providing a way to understand internal mechanics by building components without relying on heavy external libraries.
The project utilizes object-oriented encapsulation and NumPy-based vectorization to manage model state and perform mathematical operations. Its architecture emphasizes transparency by using pure-Python logic to implement linear algebra primitives and modular parameter initialization.
Features
- From-Scratch ML Model Implementations - Provides clean, from-scratch implementations of core predictive models for studying their internal logic.
- Custom Predictive Model Development - Builds bespoke predictive models from scratch using internal algorithms rather than relying on heavy external libraries.
- Educational ML Algorithm Libraries - Provides a collection of core machine learning models implemented from scratch in Python for educational purposes.
- Educational Codebases - Serves as a canonical codebase of ML algorithms designed specifically for study and reference.
- Machine Learning Education - Teaches fundamental machine learning concepts and mathematical logic through minimal, clean code implementations.
- From-Scratch Implementations - Implements mathematical models using general-purpose Python to emphasize the pedagogical value of from-scratch coding.
- Educational Python Implementations - Uses pure-Python logic to implement mathematical formulas, ensuring the internal workings are transparent and readable.
- Python Native Implementations - Implements predictive models using native Python structures to ensure the underlying mathematical logic is transparent.
- Linear Algebra Routines - Implements fundamental linear algebra primitives like matrix multiplication and dot products to compute model gradients.
- Vectorized Operations - Utilizes NumPy-based vectorization to perform high-speed mathematical operations without using explicit Python loops.
- Algorithm Implementations - Implementations of common machine learning algorithms from scratch.
- Machine Learning - Collection of machine learning algorithms implemented in Python.
