CMSC2208

This course introduces students to the fundamentals of machine learning using Python, equipping them with practical, hands-on skills that build on foundational data analytics and advanced programming proficiencies from CMSC1217 and CMSC1236. Students will explore key concepts including supervised and unsupervised learning, data preparation, model training, evaluation, and project structuring. Emphasizing a programming-focused approach, the course enables learners to create simple machine learning tools, ultimately preparing them for advanced technical roles and enhancing their employability in an Al-driven tech industry. By the end of the course, students will be able to design, implement, and evaluate basic machine learning models for real-world problem solving. Student Learning Outcomes

• Describe the fundamental programming-based concepts of machine learning, including supervised and unsupervised learning, and their practical programming applications using Python.
• Explain how programming tools automate and enhance machine learning processes for real? world datasets.
• Use Python to implement basic supervised learning models, such as linear regression or classification, on simple datasets.
• Compare different programming-based evaluation metrics to assess model performance effectively for machine learning tasks.
• Assess the effectiveness of data preparation techniques using Python for improving machine learning model outcomes.
• Develop a machine learning project using Python, integrating data preparation, a basic model, programming-based evaluation, and project structuring.