This repository provides implementations of fundamental machine learning algorithms including KNN, Linear/Logistic Regression, SVM, Decision Trees, Random Forests, Naive Bayes, Perceptron, PCA, K-Means, AdaBoost, and LDA. All algorithms are implemented from scratch in pure numpy to demonstrate the underlying mathematics, rather than relying on existing ML libraries. The project serves as a learning resource with accompanying YouTube tutorials explaining both the math and code.