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Course Outline
Introduction and Team Use Case Selection
- Overview of AI applications in industrial settings
- Categories of use cases: quality, maintenance, energy efficiency, and logistics
- Team formation and defining project scope
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
- Selecting appropriate models: 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 and reports
- Interpreting performance metrics such as accuracy, precision, and recall
- Documenting assumptions and model limitations
Deployment Simulation and Feedback
- Simulating edge and cloud deployment scenarios
- Gathering feedback and refining models
- Strategies for integrating AI into operational workflows
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 key takeaways
- Roadmap for scaling use cases within the organization
Summary and Next Steps
Requirements
- A foundational understanding of manufacturing or industrial processes
- Proficiency in Python and fundamental machine learning concepts
- Capability to manipulate both structured and unstructured data
Target Audience
- Cross-functional teams
- Engineers
- Data scientists
- IT professionals
21 Hours
