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Course Outline
Introduction and Team Use Case Selection
- Overview of AI applications in industrial settings
- Use case categories: quality control, maintenance, energy efficiency, logistics
- Forming teams and defining project objectives
Understanding and Preparing Industrial Data
- Types of industrial data: time-series, tabular, image, text
- Data acquisition, cleaning, and preprocessing techniques
- Exploratory data analysis using Pandas and Matplotlib
Model Selection and Prototyping
- Choosing appropriate methods: regression, classification, clustering, or anomaly detection
- Training and evaluating models with Scikit-learn
- Utilizing TensorFlow or PyTorch for advanced modeling
Visualizing and Interpreting Results
- Developing 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 to refine models
- Strategies for integrating AI with existing operations
Capstone Project Development
- Finalizing and testing team prototypes
- Peer review and collaborative debugging
- Preparing project presentations and technical summaries
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 work with both structured and unstructured data
Target Audience
- Cross-functional teams
- Engineers
- Data scientists
- IT professionals
21 Hours
