This course provides a comprehensive introduction to machine learning (ML) and deep learning (DL) using Python and R. Students will learn key concepts in both programming languages, along with essential data preprocessing, exploration, and visualization techniques. The course covers supervised and unsupervised learning methods, advanced ML topics, and dives deep into neural networks and deep learning architectures. Additional topics include natural language processing (NLP), time series forecasting, model deployment, and ensuring model fairness. Through hands-on projects and case studies, learners will gain practical experience in building and deploying powerful machine learning and deep learning models.