ML Projects - Full Stack Deep Learning - Spring 2021

Dive into a comprehensive lecture on setting up Machine Learning projects like a professional. Learn about the ML lifecycle, assess project feasibility and impact, explore project archetypes, and develop a keen focus on metrics and baselines. Gain insights into why ML projects fail and how to avoid common pitfalls. Follow a running case study throughout the lecture to apply concepts in real-world scenarios. Explore key topics including project prioritization, choosing appropriate metrics for optimization, and establishing effective baselines to evaluate model performance. Access an extensive summary document for further study and reference.