HJoonKwon/machine_learning_fundamentals
To study and understand machine learning fundamental algorithms.
This is a learning resource for those who want to grasp the core ideas behind machine learning algorithms. It provides hands-on Python code examples using basic numerical libraries for common machine learning models like K-Nearest Neighbors, Linear Regression, and Neural Networks. The content is designed for anyone aiming to understand how these fundamental algorithms work from the ground up, not just use them.
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Use this if you are a student, hobbyist, or aspiring data scientist who wants to learn the fundamental mathematical and computational principles of machine learning algorithms by seeing them implemented from scratch.
Not ideal if you are looking for a robust, production-ready machine learning library or a tool to quickly apply advanced machine learning techniques to real-world datasets.
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Feb 02, 2023
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