Introduction

Train an ML model with examples (Training)

• label - true answer
• model - mathematical function

Key things to measure how inclusive a machine learning system is

• Equality of Opportunity
• Simulating Decisions
• Finding Errors in your dataset using Facets

Launching into ML

Defining ML Models

• Parameter - real-valued variable that changes during model training
• Hyper-parameter - setting that we set before training and doesn't change afterwards