Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Fast and customizable framework for automatic ML model creation (AutoML)

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

SensiML's open-source AutoML solution for Edge AI model development

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

Encodings for neural architecture search

A SapientML plugin of SapientMLGenerator

a library to tune xgboost models

This repository contains an implementation of TPOT for obtaining optimal pipelines with the use of genetic algorithms.

A deep learning system that automatically designs optimal CNN architectures using Neural Architecture Search to classify lung - colon cancer from histopathology images. Achieves 99.72% accuracy across five cancer types with robust regularization. PyTorch-based solution ready for medical imaging deployment with exceptional generalization performane

Shrinkit is a powerful GUI-based Python library designed for automating machine learning tasks. With its intuitive interface, Shrinkit simplifies the process of building, training, and evaluating machine learning models, making it accessible to users of all skill levels. Shrinkit is a No-code package which can be used as a GUI.

mlopt is an AutoML library. Implementing some meta-heuristics optimization algorithms to generate fully trained and hyper parameterized ML models. The most used are Genetic Algorithm, PSO Algorithm and ACO Algorithm.

Analyze the tunability of machine learning models with Grid Search, Random Search, and Bayesian Optimization. This project explores hyperparameter tuning methods on diverse datasets, comparing efficiency, stability, and performance. Featuring Random Forest, XGBoost, Elastic Net, and Gradient Boosting.

Auto ML application app with dash. Classification and Regression algorithms.

AutoML as a Service

AutoML

Comprehensive AutoML framework that automates data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Features neural architecture search and automated data cleaning pipelines.

Automated machine learning. Evaluate a battery of binary classification algorithms across feature and hyper-parameter spaces.