Awesome scikit-learn

Machine Learning in Python

Contents

• Algorithms
• Applications
• Books
• Documentation
• Examples
• Extensions
• Frameworks
• MOOCs
• Papers
• Presentations

Algorithms

Python packages providing additional, scikit-learn API compatible implementations of algorithms.

• auto-sklearn - Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
• scikit-cmeans - C-means fuzzy clustering algorithms.
• scikit-dda - Scikit-learn-compatible Deep Discriminant Analysis.
• scikit-garden - A garden of scikit-learn compatible trees.
• scikit-keras - Scikit-learn-compatible Keras models.
• scikit-MDR - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.
• scikit-multilearn - Multi-label classification algorithms and wrappers for other libraries.
• scikit-network - Network graph algorithms.
• scikit-optimize - Methods for sequential model-based optimization.
• scikit-sos - Stochastic Outlier Selection (SOS) for detecting outliers.
• scikit-splearn - Toolbox for spectral learning algorithms.

Applications

• dash-svm - Interactive exploration of Support Vector Machine (SVM).

Books

• Advanced Machine Learning with Python (1st edition, 2016).

• Building Machine Learning Systems with Python - Companion code.

• Introduction to Machine Learning with Python (1st edition, 2016)

• Large Scale Machine Learning with Python (1st edition, 2016)

• Learning scikit-learn: Machine Learning in Python (2013)

• Machine Learning with scikit-learn Quick Start Guide (1st edition, 2018).

• Mastering Machine Learning with Python in Six Steps (2nd edition, 2019).

• Mastering Machine Learning with scikit-learn (2nd edition, 2017).

• Mastering Predictive Analytics with scikit-learn and TensorFlow (1st edition, 2018).

• Python Machine Learning Cookbock (2nd edition, 2019).

• scikit-learn Cookbook (2nd edition, 2017) - Companion code.

• Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Documentation

• Quick Start Tutorial
• User Guide
• API Reference

Examples

• plotly Example Gallery - Extensive list of examples about how to visualize data in scikit-learn workflows.
• scikit-learn Example Gallery - Official scikit-learn docs examples.

Extensions

Python packages providing functionality to help working with scikit-learn.

• scikit-datasets - Scikit-learn-compatible datasets.
• sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn.
• scikit-ext - Various estimators and separators.
• scikit-neuralnetwork - Deep neural networks without the learning cliff and classifiers and regressors compatible with scikit-learn.
• sklearn-onnx - Convert your scikit-learn model into ONNX.
• scikit-onnxruntime - Scikit-learn wrapper of onnxruntime.
• sklearn-pandas - Pandas integration with sklearn.
• scikit-plot - A visualization library for quick and easy generation of common plots in data analysis and machine learning.
• sklearn-pmml-model - A library to parse PMML models into Scikit-learn estimators.
• sklearn-porter - Transpile trained scikit-learn models to C, Java, Javascript and others.
• scikit-spark - Spark acceleration for Scikit-Learn cross validation techniques.
• scikit-vis - Adds gorgeous, easy-to-use graphics to the massively popular scikit-learn.
• sklearn-xarray - Wrappers that allow the user to apply scikit-learn estimators to xarray types (tensors) without losing their labels.
• SciPy - List of scikits - Very incomplete list of extensions for SciPy helpful e.g. for domain specific work/integration with scikit-learn.
• SciPy - Topical software - List of software which can be helpful when working in the SciPy universe.

Frameworks

Frameworks built on top of scikit-learn

• ML-ENS - High performance ensemble learning: ML-ENS combines a Scikit-learn high-level API with a low-level computational graph framework to build memory efficient, maximally parallelized ensemble networks in as few lines of codes as possible.
• modAL - A modular active learning framework.
• nilearn - Machine learning for Neuro-Imaging.
• tsfresh - Time Series Feature extraction based on scalable hypothesis tests.
• tslearn - Machine learning tools for the analysis of time series.
• Reproducible Experiment Platform (REP)
• SciKit-Learn Laboratory (SKLL)

Frameworks scikit-learn is built on top of

• SciPy - Ecosystem for mathematics, science, and engineering.
  • Matplotlib - Comprehensive 2D Plotting.
  • numpy - Base N-dimensional array package.
  • pandas - Data structures & analysis.

MOOCs

• Feature Engineering for Machine Learning (udemy.com)
• Feature Selection for Machine Learning (udemy.com)

Papers

• Scikit-learn: Machine Learning in Python (2011)
• API design for machine learning software: experiences from the scikit-learn project

Presentations

2019

• Hello World of Machine Learning Using Scikit Learn (PyCon 2019)
• Scikit-learn, wrapping your head around machine learning (PyCon 2019)

2018

• Software Library APIs: Lessons Learned from scikit-learn (PyCon Cleveland 2018)
• Machine Learning with Scikit-Learn, Part 1 (SciPy 2018 Tutorial)
• Machine Learning with scikit-learn, Part 2 (SciPy 2018 Tutorial)

2017

• Machine Learning with scikit learn (SciPy 2017)
  • Part 1/2
  • Part 2/2
  • Companion code
• Using Django, Docker and Scikit-learn to bootstrap your Machine Learning Project (PythonDay 2017 Mexico)

2016

• Learning scikit learn - An Introduction to Machine Learning in Python (PyData Chicago 2016)

2015

• A Thorough Machine Learning Pipeline via Scikit Learn (PyData Dallas 2015)
• Machine Learning with Scikit-Learn (PyData NYC 2015)
• Machine Learning with Scikit Learn (PyData Seattle 2015) - Slides.

2014

• Advanced Scikit-learn (PyData NYC 2014)

2013

• Intro to scikit-learn (SciPy 2013)
  • Part 1/3
  • Part 2/3
  • Part 3/3
  • Companion code
• Data Agnosticism: Feature Engineering Without Domain Expertise (SciPy 2013)