QuyAnh2005/homemade-machine-learning
Understand and code some basic algorithms in machine learning from scratch
This collection helps data scientists and machine learning engineers understand foundational machine learning algorithms. It offers transparent, from-scratch implementations of techniques like Linear Regression, K-means Clustering, and Decision Trees, providing a clear view of how these models process data and generate outputs. It's designed for those who want to deepen their theoretical understanding by coding these algorithms themselves.
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Use this if you are a machine learning practitioner who wants to understand the inner workings of common algorithms by studying their code implementations.
Not ideal if you need a production-ready library for deploying machine learning models or if you are looking for advanced, state-of-the-art algorithms.
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4
Language
Jupyter Notebook
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Last pushed
Mar 29, 2023
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