Machine Learning (ML) is a branch of Artificial Intelligence that enables systems to learn and improve from experience without being explicitly programmed. This tutorial introduces the fundamentals of ML, including supervised, unsupervised, and reinforcement learning techniques. You’ll learn how algorithms like linear regression, decision trees, and clustering work, along with real-world applications in recommendation systems, image recognition, and predictive analytics. The Machine Learning  tutorial also covers essential tools such as Python, NumPy, pandas, and scikit-learn for building ML models. Whether you’re a beginner or exploring AI concepts, this guide will help you understand the basics and start your ML journey.