Scikit-learn (also known as sklearn) is a machine learning Python library that features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.


Scikit-learn Tutorial
This repository contains notebooks and other files associated with my Scikit-learn tutorial.
scikit-learn code tutorial
Examples for all the different utilities within scikit-learn.
scikit-learn naive-bayes linear-regression logistic-regression
Scikit-learn Advanced Features | Data Science
It demonstrates some useful scikit-learn concepts in transforming features, pipelining, grid search, and much more.
machine-learning scikit-learn data-science feature-engineering
From Hours to Seconds: 100x Faster Boosting, Bagging, & Stacking
100x Faster Boosting, Bagging, and Stacking with RAPIDS cuML and Scikit-learn Machine Learning Model Ensembling.
rapids cuml scikit-learn emsembling


Automated Machine Learning with scikit-learn.
automl scikit-learn library research
A scikit-learn compatible neural network library that wraps pytorch
pytorch skorch scikit-learn code
A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.
unit-tests scikit-learn pandas machine-learning
Scikit-fuzzy is a fuzzy logic toolkit for SciPy.
scikit-learn fuzzy-logic scipy library
Extra blocks for sklearn pipelines.
scikit-learn library code
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