jawherr/machine-learning-algorithme
This repository contains implementations of various machine learning algorithms in Jupyter Notebook format.
This collection helps data scientists and analysts understand core machine learning algorithms through practical examples. It takes conceptual descriptions of algorithms like Linear Regression or K-Nearest Neighbors and provides working Python code within Jupyter Notebooks, enabling you to see how these models are built and applied step-by-step. It's ideal for those learning or teaching machine learning concepts.
Use this if you are a data science student, educator, or practitioner who wants to explore, understand, and experiment with foundational machine learning algorithms.
Not ideal if you are looking for production-ready code, advanced or specialized algorithms, or a ready-to-use tool for solving a specific business problem.
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Jupyter Notebook
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Last pushed
Jan 12, 2026
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