A collection of machine learning projects, jupyter notebooks and algorithms implemented from scratch as I learn and explore ML as a CS student. This repo includes various experiments, custom implementations, and tests on different datasets—focused on building intuition and deepening understanding of how ML really works under the hood.
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This repository documents my journey as a Computer Science student learning machine learning by building algorithms from scratch, experimenting with different models, and testing them on varied datasets.
💡 What you’ll find here: • ML algorithms implemented without libraries like Scikit-learn (e.g., custom linear regression, k-NN, decision trees, etc.) • Exploratory experiments with datasets (e.g., classification, regression, clustering) • Notes, mini-projects, and code that help me learn ML fundamentals
🎯 Goal:
To understand machine learning deeply by coding everything manually, making mistakes, and learning from them.