Cursusaanbod

Introduction and Team Use Case Selection

  • Overview of AI in industrial environments
  • Use case categories: quality, maintenance, energy, logistics
  • Team formation and scoping of project objectives

Understanding and Preparing Industrial Data

  • Types of industrial data: time-series, tabular, image, text
  • Data acquisition, cleaning, and preprocessing
  • Exploratory data analysis with Pandas and Matplotlib

Model Selection and Prototyping

  • Choosing between regression, classification, clustering, or anomaly detection
  • Training and evaluating models with Scikit-learn
  • Using TensorFlow or PyTorch for advanced modeling

Visualizing and Interpreting Results

  • Creating intuitive dashboards or reports
  • Interpreting performance metrics (accuracy, precision, recall)
  • Documenting assumptions and limitations

Deployment Simulation and Feedback

  • Simulating edge/cloud deployment scenarios
  • Collecting feedback and improving models
  • Strategies for integration with operations

Capstone Project Development

  • Finalizing and testing team prototypes
  • Peer review and collaborative debugging
  • Preparing project presentation and technical summary

Team Presentations and Wrap-Up

  • Presenting AI solution concepts and outcomes
  • Group reflection and lessons learned
  • Roadmap for scaling use cases within the organization

Summary and Next Steps

Vereisten

  • An understanding of manufacturing or industrial processes
  • Experience with Python and basic machine learning
  • Ability to work with structured and unstructured data

Audience

  • Cross-functional teams
  • Engineers
  • Data scientists
  • IT professionals
 21 Uren

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