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Duration 21 hours
Course Outline
Introduction and Selection of Team-Based Use Cases
- Context of artificial intelligence applications in industrial settings
- Use case domains: quality assurance, maintenance, energy management, and logistics
- Formation of collaborative teams and definition of project scope and objectives
Analysis and Preparation of Industrial Datasets
- Categories of industrial data: time-series, tabular, image, and text formats
- Processes for data collection, sanitation, and preliminary processing
- Exploratory data analysis utilizing Pandas and Matplotlib libraries
Model Selection and Prototype Development
- Selection of appropriate algorithmic approaches: regression, classification, clustering, or anomaly detection
- Training and performance evaluation using the Scikit-learn framework
- Implementation of advanced modeling techniques with TensorFlow or PyTorch
Visualization and Interpretation of Analytical Results
- Development of intuitive dashboards and reporting mechanisms
- Interpretation of key performance indicators, including accuracy, precision, and recall
- Documentation of underlying assumptions and operational limitations
Deployment Simulation and Iterative Feedback
- Simulation of edge and cloud deployment architectures
- Incorporation of feedback for continuous model improvement
- Strategies for integrating solutions into existing operational workflows
Development of Capstone Projects
- Finalization and rigorous testing of team-developed prototypes
- Peer review processes and collaborative troubleshooting sessions
- Preparation of formal project presentations and technical summaries
Team Presentations and Course Conclusion
- Presentation of AI solution concepts and demonstrated outcomes
Summary of Key Concepts and Recommended Next Steps
Requirements
- Foundational knowledge of manufacturing or industrial process workflows
- Proficiency in Python programming and basic machine learning principles
- Capability to process and analyze both structured and unstructured data sources
Target Audience
- Cross-functional project teams
- Engineers and technical specialists
- Data scientists and analysts
- Information technology professionals