Competency Framework |
Statement |
AI Competency Framework |
Understanding Data: 1.1 Employ different types of data and their representations |
AI Competency Framework |
Understanding Data: 1.2 Analyze typical uses of data in machine learning (ML) and AI |
AI Competency Framework |
Data Handling and Manipulation: 3.1 Prepare data for use in an ML or AI project |
AI Competency Framework |
Data Handling and Manipulation: 3.2 Manipulate data |
AI Competency Framework |
Core Language Skills: 1.1 Write code using proper syntax and structure |
AI Competency Framework |
Core Language Skills: 1.2 Incorporate libraries |
AI Competency Framework |
Core Language Skills: 1.3 Improve code performance |
AI Competency Framework |
Data Reprocessing: 1.1 Prepare features for use in supervised or non-supervised learning tasks |
AI Competency Framework |
Supervised Learning: 2.1 Manage a supervised learning framework |
AI Competency Framework |
Supervised Learning: 2.2 Apply supervised learning to specific tasks |
AI Competency Framework |
Unsupervised Learning: 3.1 Manage an unsupervised learning framework |
AI Competency Framework |
Artificial Neural Networks: 2.1 Use general multi-layer neural networks |
AI Competency Framework |
Artificial Neural Networks: 2.2 Use specific deep learning models |
AI Competency Framework |
Data Storage: 1.1 Manipulate data stored in files |
AI Competency Framework |
Data Storage: 2.1 Manipulate data stored in databases |
AI Competency Framework |
Cloud Computing: 3.1 Use different types of cloud architectures |
AI Competency Framework |
Tools: 3. Configure the tool |
AI Competency Framework |
Tools: 4. Find documentation for the tool |
Building Blocks Foundational Tier 1 |
this is a test 08.25.2022 |
AI Competency Framework |
AI Fundamentals: 1.1. Apply technical concepts based on hybrid AI knowledge |
AI Competency Framework |
Prototyping and Testing: 4.1 Create a prototype that integrates AI components |