NahidEbrahimian/Machine-Learning
Machine Learning algorithms Implementation from Scratch
This project helps developers understand and implement fundamental machine learning algorithms from scratch. It provides code examples for various algorithms like K-Nearest Neighbors, Adaline, Perceptron, and Multilayer Perceptron, demonstrating their application on datasets such as Iris, MNIST handwritten digits, Boston House Prices, and Titanic passenger data. It's intended for students or developers who want to learn the mathematical foundations and core mechanics of these algorithms.
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Use this if you are a developer or student looking to learn the underlying mechanics and implement machine learning algorithms from first principles.
Not ideal if you are looking for a ready-to-use tool to solve a specific business problem without needing to understand the code implementation details.
Stars
18
Forks
2
Language
Jupyter Notebook
License
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Category
Last pushed
Mar 29, 2024
Commits (30d)
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