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Course Outline
Introduction and Team Use Case Selection
- Overview of AI applications in industrial environments.
- Categories of use cases: quality, maintenance, energy, and logistics.
- Team formation and scoping of project objectives.
Understanding and Preparing Industrial Data
- Types of industrial data: time-series, tabular, image, and text.
- Data acquisition, cleaning, and preprocessing techniques.
- Exploratory data analysis using Pandas and Matplotlib.
Model Selection and Prototyping
- Choosing between regression, classification, clustering, or anomaly detection.
- Training and evaluating models with Scikit-learn.
- Utilizing 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
Requirements
- Familiarity with manufacturing or industrial processes.
- Proficiency in Python and foundational machine learning concepts.
- Capability to handle both structured and unstructured data.
Target Audience
- Cross-functional teams.
- Engineers.
- Data scientists.
- IT professionals.
21 Hours
