🥧ReciPys: easily define and execute preprocessing and feature engineering steps on Pandas dataframes.
MIT License
General Assembly's 2015 Data Science course in Washington, DC
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
Improved pipelines for data science projects.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-co...
机器学习教程,本教程包含基于numpy、sklearn与tensorflow机器学习,也会包含利用spark、flink加快模型训练等用法。本着能够较全的引导读者入门机器学习。
Feature engineering package with sklearn like functionality
Natural Intelligence is still a pretty good idea.
Some fundamental machine learning and data-analysis techniques are explained through realistic ex...
A Python library with utilities for Machine Learning research and algorithm implementations
A framework for prototyping and benchmarking imputation methods
Supercharged pandas indexing
Data science tools for exploration, visualization, and model iteration.
Awesome Domain Adaptation Python Toolbox
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Helper functions for all stages of the machine learning cycle.