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Duration 21 hours
Course Outline
Introduction and Team Selection of Use Cases
- Overview of AI applications in industrial settings
- Categories of use cases: quality, maintenance, energy, logistics
- Team formation and defining project scope
Comprehending and Preparing Industrial Data
- Varieties of industrial data: time-series, tabular, image, text
- Data collection, cleansing, and preprocessing techniques
- Exploratory data analysis using Pandas and Matplotlib
Model Selection and Prototyping
- Selecting appropriate methods: regression, classification, clustering, or anomaly detection
- Training and assessing models with Scikit-learn
- Leveraging TensorFlow or PyTorch for advanced modelling
Visualising and Interpreting Results
- Developing intuitive dashboards or reports
- Analysing performance metrics (accuracy, precision, recall)
- Documenting underlying assumptions and constraints
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 results
- Group reflection and key takeaways
- Roadmap for scaling use cases within the organisation
Summary and Next Steps
Requirements
- Familiarity with manufacturing or industrial workflows.
- Proficiency in Python and foundational machine learning concepts.
- Competence in handling both structured and unstructured data.
Target Audience
- Cross-functional teams
- Engineers
- Data scientists
- IT specialists
Testimonials (1)
How to use and explore AI for daily works
