luwill/Machine_Learning_Code_Implementation
Mathematical derivation and pure Python code implementation of machine learning algorithms.
This resource provides comprehensive mathematical derivations and pure Python code implementations for 26 classic machine learning algorithms, categorized into supervised, unsupervised, and probabilistic models. It takes theoretical concepts from textbooks and translates them into practical, executable code. Students and self-learners of machine learning who are looking to deepen their understanding of algorithms beyond theoretical explanations would find this useful.
1,551 stars. No commits in the last 6 months.
Use this if you are studying machine learning and want to understand the detailed mathematical logic and see the corresponding Python code implementation for various algorithms.
Not ideal if you are a practitioner looking for high-level, production-ready machine learning libraries like scikit-learn or TensorFlow.
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Jupyter Notebook
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Sep 18, 2024
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