Atlantic: Automated Data Preprocessing Framework for Supervised Machine Learning
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Updated
Jun 17, 2024 - Python
Atlantic: Automated Data Preprocessing Framework for Supervised Machine Learning
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
TSForecasting: Automated Time Series Forecasting Framework
Powerful AutoML toolkit
Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data
The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.
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.
Library for streaming data and incremental learning algorithms.
Sugar candy for data scientist. Easy manipulation in time-series data analytics works.
Knowledge-driven AutoML
Utilizes pycaret to automates machine learning workflows (Deployed at streamlit)
Projeto de criação de modelo de machine learning para score de credito, percorrendo todo o pipeline dos dados. Coleta, exploração, tratamento, limpeza, treino e deploy.
TinyAutoML is a comprehensive Pipeline Classifier Project thought as a Scikit-learn plugin
AutoML as a Service.
Automating the ML Training Lifecycle with MLxOPS
Auto torch image models: train and evaluation
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
A GitHub compiling the input data, Python and Jupyter Notebook scripts, and all relevant statistical outputs from running the AutoMLPipe-BC automated machine learning pipeline (from the Urbanowicz Lab - https://github.com/UrbsLab) on a large-scale single nucleotide polymorphism (SNP) dataset from patients with congenital heart disease (CHD)
Auto Machine learning platform as seen on https://www.youtube.com/watch?v=JHJLLiMnz6A
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