As the demand for flexible Machine Learning (ML) education grows among working professionals, optimizing self-paced learning models becomes crucial. This study investigates effective strategies for self-paced ML education by conducting a systematic review of academic literature, analyzing existing course websites, and integrating insights from in-depth interviews with 21 professionals. Key findings reveal that a modular course structure, hands-on projects with real-world datasets, comprehensive learning resources, and ongoing support significantly enhance learning outcomes. By addressing these elements, this research provides actionable recommendations for developing effective self-paced ML courses, ultimately supporting the continuous professional development and career advancement of learners in the field of ML.