Learn how to select the most appropriate machine learning model in this comprehensive 30-minute video lecture. Explore key aspects of model selection, including comparing different models, distinguishing between signal and noise, selecting an appropriate loss function, implementing effective data splitting techniques, and understanding model assumptions. Gain valuable insights to enhance your decision-making process when choosing the optimal machine learning model for your specific use case.
Overview
Syllabus
How to pick a machine learning model 1: Choosing between models.
How to pick a machine learning model 2: Separating signal from noise.
How to pick a machine learning model 3: Choosing a loss function.
How to pick a machine learning model 4: Splitting the data.
How to pick a machine learning model 5: Navigating assumptions.
Taught by
Brandon Rohrer
