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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

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