# wepe/machinelearning

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5,714 stars · 3,209 forks · Python

## Links

- GitHub: https://github.com/wepe/MachineLearning
- awesome-repositories: https://awesome-repositories.com/repository/wepe-machinelearning.md

## Description

This project is a machine learning library providing a collection of implementations for supervised and unsupervised learning algorithms. It serves as a deep learning framework, a statistical classifier collection, and a suite of tools for unsupervised learning and dimensionality reduction.

The library enables the construction of neural networks, including multi-layer perceptrons and convolutional networks for pattern recognition. It also provides tools for performing principal component analysis and manifold learning to visualize high-dimensional datasets, alongside a suite of clustering algorithms that group unlabeled data through iterative partitioning.

The project covers a broad range of predictive modeling capabilities, including classification and regression tasks using decision trees, k-nearest neighbors, Bayes classifiers, support vector machines, and ridge regression. It also includes tools for image classification workflows and the analysis of unlabeled data.

## Tags

### Artificial Intelligence & ML

- [General Deep Learning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/general-deep-learning-frameworks.md) — Serves as a deep learning framework for building multi-layer perceptrons and convolutional networks.
- [Supervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-learning.md) — Provides a comprehensive collection of supervised learning algorithms for classification and regression tasks.
- [Bayesian Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/bayesian-inference.md) — Predicts class membership using probabilistic Bayesian inference and conditional probability theorems.
- [Centroid-Based Clustering](https://awesome-repositories.com/f/artificial-intelligence-ml/centroid-based-clustering.md) — Groups unlabeled data by iteratively calculating and updating cluster centers.
- [Clustering Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/clustering-suites.md) — Offers a collection of centroid and density-based clustering implementations for unlabeled data.
- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Implements convolutional processing to extract spatial features from images for pattern recognition.
- [Decision Trees](https://awesome-repositories.com/f/artificial-intelligence-ml/decision-trees.md) — Builds classification trees using iterative feature splitting to make data predictions.
- [Deep Learning Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-architectures.md) — Provides frameworks for constructing multi-layered neural networks to identify complex patterns. ([source](https://github.com/wepe/machinelearning#readme))
- [Deep Learning Development](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-development.md) — Provides tools for the design, construction, and training of multi-layered artificial neural networks.
- [Clustering Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/k-means-clustering/clustering-algorithms.md) — Provides clustering algorithms including KMeans and Gaussian Mixture Models for grouping unlabeled data. ([source](https://github.com/wepe/machinelearning#readme))
- [K-Nearest Neighbor Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/k-nearest-neighbor-classifiers.md) — Provides k-nearest neighbor classifiers that assign classes based on proximity to training samples. ([source](https://github.com/wepe/machinelearning#readme))
- [Logistic Regression Models](https://awesome-repositories.com/f/artificial-intelligence-ml/logistic-regression-models.md) — Implements logistic regression models to predict binary outcomes using the sigmoid function. ([source](https://github.com/wepe/machinelearning#readme))
- [Machine Learning Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-libraries.md) — Provides a comprehensive collection of supervised and unsupervised machine learning algorithmic implementations.
- [Multi-Layer Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/multi-layer-architectures.md) — Constructs deep learning models using multiple hidden layers to extract hierarchical patterns.
- [Multilayer Perceptrons](https://awesome-repositories.com/f/artificial-intelligence-ml/multilayer-perceptrons.md) — Implements multi-layer perceptrons with fully connected layers for learning non-linear mappings. ([source](https://github.com/wepe/machinelearning#readme))
- [Naive Bayes Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/naive-bayes-classifiers.md) — Implements naive bayes classifiers based on statistical feature distributions and conditional probability. ([source](https://github.com/wepe/machinelearning#readme))
- [Statistical Classifier Collections](https://awesome-repositories.com/f/artificial-intelligence-ml/statistical-classifier-collections.md) — Implements a diverse set of statistical classifiers including Bayes, support vector machines, and k-nearest neighbors.
- [Dimensionality Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-classification/dimensionality-reduction.md) — Provides techniques for projecting high-dimensional data into lower-dimensional spaces to improve model efficiency.
- [Support Vector Machines](https://awesome-repositories.com/f/artificial-intelligence-ml/support-vector-machines.md) — Implements support vector machines to find optimal decision boundaries through boundary-based vector optimization.
- [Unsupervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/unsupervised-learning.md) — Provides algorithms for discovering patterns and structures in unlabeled datasets through clustering. ([source](https://github.com/wepe/machinelearning#readme))
- [Image Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification.md) — Implements convolutional neural networks to categorize images based on visual content.

### Scientific & Mathematical Computing

- [Principal Component Analysis](https://awesome-repositories.com/f/scientific-mathematical-computing/linear-algebra-routines/principal-component-analysis.md) — Implements principal component analysis to isolate primary patterns by reducing dataset dimensionality. ([source](https://github.com/wepe/machinelearning#readme))
- [Ridge Regression](https://awesome-repositories.com/f/scientific-mathematical-computing/ridge-regression.md) — Implements ridge regression with L2 regularization to prevent overfitting in linear models. ([source](https://github.com/wepe/machinelearning#readme))

### Data & Databases

- [Manifold Visualizations](https://awesome-repositories.com/f/data-databases/data-analysis-visualization/visualization-frameworks-libraries/data-visualization/three-dimensional-visualizations/dimensionality-projection-plots/manifold-visualizations.md) — Projects high-dimensional data into low-dimensional space via manifold learning for visual analysis. ([source](https://github.com/wepe/machinelearning#readme))
- [Manifold Learning](https://awesome-repositories.com/f/data-databases/multi-dimensional-analysis/high-dimensional-distribution-analysis/manifold-learning.md) — Implements manifold learning to visualize complex, high-dimensional datasets in lower dimensions.
- [Dimensionality Reduction](https://awesome-repositories.com/f/data-databases/vector-quantization/high-dimensional-vector-compressors/dimensionality-reduction.md) — Implements principal component analysis and other dimensionality reduction techniques to simplify complex datasets.