Machine Learning in Python
- Algorithms
- Applications
- Books
- Documentation
- Examples
- Extensions
- Frameworks
- MOOCs
- Papers
- Presentations
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.
- dash-svm - Interactive exploration of Support Vector Machine (SVM).
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Building Machine Learning Systems with Python - Companion code.
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Introduction to Machine Learning with Python (1st edition, 2016)
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Large Scale Machine Learning with Python (1st edition, 2016)
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Machine Learning with scikit-learn Quick Start Guide (1st edition, 2018).
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Mastering Machine Learning with Python in Six Steps (2nd edition, 2019).
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Mastering Machine Learning with scikit-learn (2nd edition, 2017).
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Mastering Predictive Analytics with scikit-learn and TensorFlow (1st edition, 2018).
- 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.
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.
- 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)
- SciPy - Ecosystem for mathematics, science, and engineering.
- Matplotlib - Comprehensive 2D Plotting.
- numpy - Base N-dimensional array package.
- pandas - Data structures & analysis.
- Feature Engineering for Machine Learning (udemy.com)
- Feature Selection for Machine Learning (udemy.com)
- Scikit-learn: Machine Learning in Python (2011)
- API design for machine learning software: experiences from the scikit-learn project
- Hello World of Machine Learning Using Scikit Learn (PyCon 2019)
- Scikit-learn, wrapping your head around machine learning (PyCon 2019)
- 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)
- Machine Learning with scikit learn (SciPy 2017)
- Using Django, Docker and Scikit-learn to bootstrap your Machine Learning Project (PythonDay 2017 Mexico)
- 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.
- Intro to scikit-learn (SciPy 2013)
- Data Agnosticism: Feature Engineering Without Domain Expertise (SciPy 2013)