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Course Outline
Introductory Session and Selecting Team Use Cases
- Overview of AI applications in industrial settings
- Categories of use cases: quality, maintenance, energy, and logistics
- Forming teams and defining project goals
Comprehending and Preparing Industrial Data
- Varieties of industrial data: time-series, tabular, images, and text
- Data collection, cleaning, and preprocessing techniques
- Conducting exploratory data analysis using Pandas and Matplotlib
Choosing Models and Building Prototypes
- Selecting appropriate methods: regression, classification, clustering, or anomaly detection
- Training and assessing models with Scikit-learn
- Utilizing TensorFlow or PyTorch for complex modeling
Visualizing and Analyzing Outcomes
- Developing intuitive dashboards or reports
- Interpreting performance indicators (accuracy, precision, recall)
- Recording assumptions and identifying limitations
Deployment Simulation and Review
- Simulating edge and cloud deployment scenarios
- Gathering feedback and refining models
- Approaches for integrating solutions into operational workflows
Developing Capstone Projects
- Finalizing and testing team prototypes
- Peer review and joint debugging sessions
- Preparing project presentations and technical summaries
Team Presentations and Conclusion
- Presenting AI solution concepts and results
- Group reflection on key takeaways
- Strategic roadmap for expanding use cases across the organization
Recap and Future Actions
Requirements
- Familiarity with manufacturing or industrial workflows
- Proficiency in Python and foundational machine learning concepts
- Competence in managing both structured and unstructured data
Target Audience
- Multi-disciplinary teams
- Engineers
- Data scientists
- IT specialists
21 Hours
