This course introduces the basic concepts and methods of machine learning. Topics include machine learning techniques such as K-Nearest Neighbors (KNN), decision trees, Naïve Bayes, linear regression, logistic regression, support vector machines, clustering, and neural networks. The course also covers evaluating model performance using appropriate metrics. Students will gain hands-on experience using Python and the Scikit-learn library, and will implement machine learning models on real-world datasets.