This repository is a complete guide to machine learning, featuring detailed implementations and explanations of key algorithms and techniques across different learning paradigms. From supervised and unsupervised learning to neural networks, ensemble methods, optimization algorithms, and more, this repository serves as a valuable resource for anyone looking to deepen their understanding of machine learning. Whether you're exploring anomaly detection, natural language processing, or model evaluation, you'll find a wide range of topics covered with practical examples and insights.