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Course Outline
Introduction and Selection of Team Use Cases
- Overview of AI applications in industrial settings
- Categories of use cases: quality, maintenance, energy, and logistics
- Team formation and defining 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
- Selecting appropriate approaches: regression, classification, clustering, or anomaly detection
- Training and evaluating models with Scikit-learn
- Utilising TensorFlow or PyTorch for advanced modelling
Visualising and Interpreting Results
- Creating intuitive dashboards or reports
- Interpreting performance metrics (accuracy, precision, recall)
- Documenting assumptions and limitations
Deployment Simulation and Feedback
- Simulating edge and cloud deployment scenarios
- Gathering feedback and refining models
- Strategies for operational integration
Capstone Project Development
- Finalising and testing team prototypes
- Peer review and collaborative debugging
- Preparing project presentations and technical summaries
Team Presentations and Conclusion
- Presenting AI solution concepts and outcomes
- Group reflection and key takeaways
- Roadmap for scaling use cases within the organisation
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
Audience
- Cross-functional teams
- Engineers
- Data scientists
- IT professionals
21 Hours
