zotroneneis/machine_learning_basics
Machine Learning Basics
Este proyecto es una colección de algoritmos y herramientas fundamentales de machine learning implementados desde cero en Python. Sirve como una librería de implementaciones centrales para modelos de regresión, clasificación y clustering, diseñada para demostrar las estructuras matemáticas subyacentes de estos algoritmos sin depender de frameworks de machine learning de alto nivel.
El proyecto se centra en la implementación manual de la lógica algorítmica, incluyendo redes neuronales con propagación hacia adelante y actualizaciones de pesos, así como varios modelos de aprendizaje supervisado y no supervisado. Utiliza NumPy para la vectorización para realizar cálculos matriciales y operaciones matemáticas en grandes datasets.
El toolkit cubre una amplia gama de capacidades, incluyendo reducción de dimensionalidad mediante Análisis de Componentes Principales (PCA) y preprocesamiento de datos para datasets numéricos y de imágenes. Las implementaciones algorítmicas abarcan regresión lineal y bayesiana, clustering K-Means y múltiples métodos de clasificación como Máquinas de Vectores de Soporte (SVM), Árboles de Decisión y K-Vecinos Más Cercanos (KNN).
El proyecto se entrega como una serie de Jupyter Notebooks.
Features
- From-Scratch ML Model Implementations - Provides foundational implementations of machine learning models built from first principles without relying on high-level frameworks.
- Machine Learning Education - Serves as an educational resource for learning the mathematics of ML by building algorithms from scratch.
- Centroid-Based Clustering - Implements K-Means clustering by iteratively refining center coordinates based on the mean of assigned points.
- Classification Trees - Implements decision trees specifically for categorizing instances into discrete classes through binary splits.
- Gradient-Based Parameter Updates - Implements weight adjustment logic using gradient descent to minimize prediction error.
- Hyperplane Margin Maximization - Implements the mathematical logic for maximizing margins in Support Vector Machines.
- K-Means Clustering - Implements centroid-based partitioning of unlabeled data into a specified number of clusters using iterative refinement.
- K-Nearest Neighbor Classifiers - Implements supervised learning models that assign classes based on the majority vote of the closest training samples.
- Linear Regression - Implements fundamental statistical methods for modeling relationships between variables using linear equations.
- Bayesian Regressions - Implements probabilistic linear models that treat parameters as distributions to quantify prediction uncertainty.
- Logistic Regression Models - Implements algorithms for predicting binary outcomes using the sigmoid function and weight optimization.
- Machine Learning Implementations - Provides a comprehensive collection of core machine learning algorithms implemented manually in Python.
- Forward Propagation Engines - Implements forward propagation logic using matrix multiplication to calculate layer outputs.
- Neural Network Implementations - Implements an artificial neural network from scratch, demonstrating forward propagation and weight updates.
- Supervised Classification - Implements supervised classification and regression models including Decision Trees and SVMs.
- Support Vector Machines - Implements support vector machines to establish optimal decision boundaries that maximize the margin between classes.
- Unsupervised Learning - Implements unsupervised learning algorithms like K-Means to discover patterns in unlabeled data.
- Educational Python Implementations - Provides algorithmic implementations in pure Python to ensure the internal logic and mathematics are transparent and readable.
- Softmax Regression - Provides an implementation of softmax regression for multi-class classification probability prediction.
- NumPy-Based Linear Algebra Implementations - Uses NumPy for vectorization and matrix manipulations to perform efficient mathematical operations on datasets.
- Perceptrons - Implements a basic linear classifier that learns a weight vector to separate two classes of data.
- Posterior Distribution Estimators - Implements Bayesian linear regression by calculating posterior probabilities for linear coefficients using prior beliefs.
- Dimensionality Reduction - Provides mathematical techniques for reducing high-dimensional data into lower-dimensional spaces based on variance.
- Principal Component Analysis - Implements Principal Component Analysis to reduce dimensionality by transforming variables into uncorrelated components.
