Abstract
The integration of machine learning (ML) techniques in education and skill development has revolutionized traditional teaching and learning paradigms. This chapter explores transformative applications of ML in education and skill development, focusing on experimental approaches to enhance learning outcomes and skill acquisition. It reviews literature on ML-driven personalized learning, intelligent tutoring systems, educational data mining, and predictive analytics, while discussing challenges and ethical considerations in implementing ML in educational settings. This chapter explores experimental methodologies for evaluating the effectiveness of ML-driven interventions, analyzing case studies and real-world examples. It highlights the design, implementation, and outcomes of these experiments, providing insights into ML's impact on student engagement, knowledge retention, skill advancement, and overall educational quality.
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