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Course Outline
Introduction to Artificial Intelligence in Quality Control for Government
- Overview of AI applications in manufacturing quality processes for government
- Applications in inspection, defect detection, and regulatory compliance for government
- Benefits and limitations of AI-powered quality assurance (QA) for government
Collecting and Preparing Quality Data for Government
- Types of data used in QA for government (images, sensors, production logs)
- Labeling visual datasets with LabelImg for government use
- Data storage and structure for training models for government applications
Introduction to Computer Vision for Quality Assurance in Government
- Basics of image processing with OpenCV for government
- Preprocessing techniques for industrial images for government
- Extracting visual features for analysis for government
Machine Learning for Anomaly Detection in Government
- Training simple classifiers for defect detection for government
- Using convolutional neural networks (CNNs) for government applications
- Unsupervised learning for anomaly identification for government
Yield Forecasting with AI Models for Government
- Introduction to regression techniques for government
- Building models to forecast production yields for government
- Evaluating and improving prediction accuracy for government
Integrating AI with Production Systems for Government
- Deployment options for inspection models in government environments
- Edge AI vs. cloud-based analysis for government operations
- Automating alerts and quality reporting for government
Practical Case Study and Final Project for Government
- Developing an end-to-end AI inspection prototype for government use
- Training and testing with sample QA datasets for government
- Presenting a functional quality control AI solution for government
Summary and Next Steps for Government
Requirements
- Knowledge of fundamental manufacturing or quality assurance processes for government
- Experience with spreadsheets or digital reporting tools
- Enthusiasm for data-driven quality control techniques
Audience
- Quality assurance specialists
- Production supervisors
21 Hours
