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awesome-scikit-learn
A curated list of awesome scikit-learn resources and libraries
https://github.com/fkromer/awesome-scikit-learn
Last synced: 3 days ago
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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.
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Books
- Advanced Machine Learning with Python (1st edition, 2016)
- Building Machine Learning Systems with Python - [Companion code](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython).
- 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](https://github.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition).
- Machine Learning with scikit-learn Quick Start Guide (1st edition, 2018)
- 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](https://github.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition).
- Advanced Machine Learning with Python (1st edition, 2016)
- Building Machine Learning Systems with Python - [Companion code](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython).
- Large Scale Machine Learning with Python (1st edition, 2016)
- Learning scikit-learn: Machine Learning in Python (2013)
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Documentation
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Examples
- scikit-learn Example Gallery - Official scikit-learn docs examples.
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Extensions
- scikit-datasets - Scikit-learn-compatible datasets.
- sklearn-onnx - Convert your scikit-learn model into ONNX.
- scikit-onnxruntime - Scikit-learn wrapper of onnxruntime.
- 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.
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Frameworks
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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.
- SciKit-Learn Laboratory (SKLL)
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Frameworks scikit-learn is built on top of
- Matplotlib - Comprehensive 2D Plotting.
- numpy - Base N-dimensional array package.
- pandas - Data structures & analysis.
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MOOCs
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Frameworks scikit-learn is built on top of
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Papers
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Frameworks scikit-learn is built on top of
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Presentations
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2019
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2018
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2017
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2016
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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](https://github.com/jakevdp/sklearn_pydata2015).
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2014
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2013
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Applications
- dash-svm - Interactive exploration of Support Vector Machine (SVM).
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