Machine learning education often involves complex topics that can be challenging to teach engagingly, leading to difficulties in maintaining student focus and achieving optimal learning outcomes. This study aims to bridge the gap between machine learning-specific teaching techniques and those centred on student engagement by conducting a comprehensive analysis of related works and an empirical experiment. The related works section reveals differences between traditional and engagement-focused teaching methods. To address the knowledge gap regarding the impact of engagement-focused methods on learning outcomes, a controlled experiment was conducted, comparing a conventional 16-minute video lecture followed by practice questions against the same content divided into four shorter video segments, each followed by a subset of the practice questions. The results demonstrate that the experimental group achieved significantly higher average quiz scores and reported consistently higher satisfaction ratings, suggesting that even a simple engagement-boosting technique can substantially improve learning outcomes and student satisfaction in machine learning education. This study highlights the importance of prioritising student engagement as the field of machine learning continues to evolve.