Explore a comprehensive lecture on setting up machine learning projects, focusing on best practices for planning and implementation. Learn about a framework for understanding ML projects, using pose estimation for robotic grasping as a running case study. Discover key points for prioritizing projects, choosing appropriate metrics, and creating effective human baselines. Gain insights into the lifecycle of ML projects, product design considerations, and strategies for combining metrics. Understand the importance of accuracy requirements and how to assess project feasibility. Delve into practical examples and engage with questions to deepen your understanding of ML project setup and management.