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Documentation for scikit-learn, a Python library for machine learning.
Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a
scikit-learn: machine learning in Python — scikit-learn 1.5.0 documentation
scikit-learn Cookbook Second Edition Over 80 recipes for machine learning in Python with scikit-learn Julian Avila Trent Hauck BIRMINGHAM - MUMBAI
Lab Objective: The scikit-learn package is the one of the fundamental tools in Python for machine learning. In this appendix we highlight and give examples of some of the more popular scikit-learn tools for classification and regression, training and testing, and complex model construction.
Scikit-Learn (Sklearn) is a powerful and robust open-source machine learning library for Python. Sklearn provides tools for efficient implement of classification, regression, clustering and dimensionality reduction techniques. Sklearn has a clean and uniform API as well as complete online documentation.
What is scikit-learn? I Simple and efficient tools for predictive data analysis I Machine Learning methods I Data processing I Visualization I Accessible to everybody, and reusable in various contexts I Documented API with lot’s of examples I Not bound to Training frameworks (e.g. Tensorflow, Pytorch) I Building blocks for your data analysis
