Case context: You are designing an onboarding curriculum for a new group of machine learning engineers. The team will eventually build modern deep learning products, but many members are still learning the basics of model development.

Question: How should you sequence the curriculum for this team, and why?

Sample answer: The curriculum should begin with general machine learning strategies that apply to both traditional algorithms and neural networks. After the team understands those foundational ideas, the program should move to more modern techniques that are especially useful for deep learning systems. This order helps the team build a stable foundation before tackling more specialized methods.

Key points:

• Start with broad strategies first.
• Cover ideas that work for both classical models and neural networks.
• Introduce deep learning-specific strategies later.
• The progression should move from general to specialized.

Rubric: The response must recommend starting with general strategies that apply across model types before advancing to modern deep learning strategies, and must explain that this order builds a foundation first.