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- scikit-learn: machine learning in Python — scikit-learn 1. 7. 1 documentation
Applications: Transforming input data such as text for use with machine learning algorithms Algorithms: Preprocessing, feature extraction, and more
- scikit-learn · PyPI
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed See the About us page for a list of core contributors
- Scikit Learn Tutorial - GeeksforGeeks
Scikit-learn (also known as sklearn) is a widely-used open-source Python library for machine learning It builds on other scientific libraries like NumPy, SciPy and Matplotlib to provide efficient tools for predictive data analysis and data mining
- Getting Started — scikit-learn 1. 7. 0 documentation - sklearn
The purpose of this guide is to illustrate some of the main features that scikit-learn provides It assumes a very basic working knowledge of machine learning practices (model fitting, predicting, cross-validation, etc ) Please refer to our installation instructions for installing scikit-learn
- Sklearn – An Introduction Guide to Machine Learning
Sklearn (scikit-learn) is a Python library that provides a wide range of unsupervised and supervised machine learning algorithms It is also one of the most used machine learning libraries and is built on top of SciPy
- Installing scikit-learn — scikit-learn 1. 7. 1 documentation
The Debian Ubuntu package is split in three different packages called python3-sklearn (python modules), python3-sklearn-lib (low-level implementations and bindings), python-sklearn-doc (documentation)
- scikit-learn: machine learning in Python — scikit-learn 0. 16. 1 . . .
Non-negative matrix factorization (NMF or NNMF)
- What is Scikit-Learn (Sklearn)? - IBM
Scikit-learn, or sklearn, is an open source project and one of the most used machine learning (ML) libraries today Written in Python, this data science toolset streamlines artificial intelligence (AI) ML and statistical modeling with a consistent interface
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