Machine Learning Specialist

Exam Content Guide

Data Sources (30%)

• Create a project in Model Studio
• Explore the data
• Modify data
• Reduce the dimensionality of the data
• Use the VARIABLE SELECTION node to identify important variables

Building Models (50%)

• Describe key supervised machine learning terms and concepts
• Build models with decision trees and ensemble of trees
• Build models with neural networks
• Build models with support vector machines
• Use Model Interpretability tools to explain black box models
• Incorporate externally written code

Model Assessment and Deployment (20%)

• Explain the principles of Model Assessment
• Assess and compare models in Model Studio
• Deploy a model