The Foundations of Machine Learning course is designed to provide a comprehensive introduction to the principles, techniques, and real-world applications of machine learning (ML). The course begins by laying the groundwork with a clear distinction between artificial intelligence, machine learning, and deep learning, and introduces the different types of learning paradigms: supervised, unsupervised, and reinforcement learning. Students learn about the life cycle of a machine learning project, including problem formulation, data collection, exploratory data analysis, feature engineering, model training, validation, and deployment. Core concepts such as linear regression, logistic regression, decision trees, support vector machines, and nearest neighbors are covered in depth. Each algorithm is presented with intuitive explanations, mathematical foundations, and implementation examples using Python and libraries like Scikit-learn. Emphasis is placed on the bias-variance trade-off, overfitting, underfitting, and model evaluation metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. The course also introduces model selection techniques like cross-validation, hyperparameter tuning using grid/random search, and ensemble methods like bagging, boosting, and stacking. Students gain hands-on experience through coding exercises and projects such as spam detection, house price prediction, and customer segmentation. Ethical considerations such as fairness, interpretability, and responsible AI are woven throughout the curriculum. By the end, learners are equipped with the theoretical understanding and practical skills to build, evaluate, and deploy machine learning models across a range of applications. This course is ideal for beginners in data science, aspiring ML engineers, and technical professionals seeking a solid foundation in machine learning principles. It serves as a prerequisite for more advanced topics like deep learning, natural language processing, and model deployment pipelines.