This repository contains Jupyter notebooks implementing common machine learning algorithms from scratch or using standard libraries. Topics covered include linear and logistic regression, decision trees, random forests, SVM, XGBoost, k-means and hierarchical clustering, PCA, LDA, and other classical ML techniques. The code serves as educational examples for understanding how these algorithms work in practice.