What is the best way for students to build practical AI skills during college ?

Building practical AI skills requires more than understanding concepts from textbooks. Students should combine programming, mathematics, data handling, machine learning fundamentals, and project-based learning. A good starting point is to become comfortable with programming and gradually work with real datasets. Students can then explore supervised learning, unsupervised learning, model evaluation, data visualization, and basic AI applications. Projects are especially valuable. Instead of completing only simple tutorial exercises, students can create projects that solve practical problems. Examples include recommendation systems, prediction models, image classification applications, chatbots, and data analysis tools. Students should also maintain a portfolio containing their best projects. Explaining the problem, technology used, development process, results, and limitations can demonstrate practical understanding to employers or higher-education institutions. Students interested in structured undergraduate learning can explore the B.Sc. CS Artificial Intelligence and Machine Learning program from MH Cognition. MH Cognition also provides educational resources covering AI, machine learning, data science, and technology careers. The most important point is consistency. AI is a broad field, so students should first develop strong fundamentals and then gradually move toward specialized areas.