This advanced course covers unsupervised learning, recommendation systems, and time series analysis. Students will explore clustering, dimensionality reduction, anomaly detection, and LSTM networks for forecasting. Using Python and libraries like scikit-learn and TensorFlow, students will work on real-world datasets to build and optimize machine learning models. The course includes hands-on projects focused on collaborative filtering, content-based, and hybrid recommendation systems. By the end, students will be equipped to solve practical problems in fields like finance, healthcare, and e-commerce