This chapter introduces the fundamentals of Machine Learning (ML), connecting theoretical principles with practical implementation. It covers supervised, unsupervised, and reinforcement learning paradigms with their mathematical foundations and algorithm designs. The material examines core concepts including bias-variance tradeoff, regularization, feature engineering, and model evaluation. Through Python implementations, readers learn to apply key algorithms such as k-Nearest Neighbors, linear regression, and clustering methods. The chapter addresses critical aspects of the ML workflow from data preparation to model deployment. Practical examples demonstrate solutions to real-world problems while highlighting performance optimization techniques. The content also examines ethical considerations including privacy protection and bias mitigation.