Repository Index

Fast and Accurate ML in 3 Lines of Code

• automl
• machine-learning
• data-science
• deep-learning

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

• machine-learning
• python
• data-science
• automl

Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.

• data-science
• python
• machine-learning
• pycaret

AutoML library for deep learning

• automl
• neural-architecture-search
• automated-machine-learning
• python

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

• h2o
• machine-learning
• data-science
• deep-learning

Lightning ⚡️ fast forecasting with statistical and econometric models.

• time-series
• statistics
• forecasting
• arima

Scalable and user friendly neural :brain: forecasting algorithms.

• deep-learning
• forecasting
• esrnn
• nbeats

Automatic architecture search and hyperparameter optimization for PyTorch

• automl
• deep-learning
• tabular-data
• pytorch

Machine Learning in R

• machine-learning
• data-science
• tuning
• cran

An autoML framework & toolkit for machine learning on graphs.

• graph-neural-networks
• automl
• pytorch
• pytorch-geometric

Use evolutionary algorithms instead of gridsearch in scikit-learn

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

• python
• pytorch
• machine-learning
• deep-learning

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

• python
• scikit-learn
• machine-learning
• artificial-intelligence

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

• svm
• regression
• classification
• machine-learning