Machine Learning & Artificial Intelligence

This course introduces learners to how intelligent systems learn from data, recognize patterns, and make predictions or decisions. Students start with foundational concepts—data, features, training, testing, and evaluation—then progress into building simple machine learning models that solve practical problems. Learners understand the full workflow: preparing data, choosing models, training them, evaluating results, and improving performance through iteration. The course also explores where AI is applied across industries like finance, health, agriculture, business, and technology—so learners understand real use cases, not just theory. Strong emphasis is placed on responsible AI: bias, privacy, ethics, and proper interpretation of model outputs. By the end, learners can build basic ML solutions, explain their results clearly, and confidently move into advanced learning or AI-assisted roles.