Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

[UNMAINTAINED] Automated machine learning for analytics & production

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

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

State-of-the art Automated Machine Learning python library for Tabular Data

MLBox is a powerful Automated Machine Learning python library.

Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL [email protected]

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Automatic machine learning for tabular data. ⚡🔥⚡

EvalML is an AutoML library written in python.

An automatic ML model optimization tool.

Library for Semi-Automated Data Science

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

LAMA - automatic model creation framework

Predict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

A full pipeline AutoML tool for tabular data

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

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