This repository contains a series of Jupyter notebooks that reproduce the examples from the textbook ‘The Elements of Statistical Learning.’ The notebooks implement algorithms including linear regression, neural networks, support vector machines, random forests, k-means clustering, and Gaussian mixture models using libraries such as numpy, sklearn, tensorflow, and scipy. It serves as an educational resource for understanding core statistical learning concepts through working code examples.