Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction and Team Use Case Selection
- Overview of AI in industrial environments.
- Use case categories: quality, maintenance, energy, logistics.
- Team formation and scoping of project objectives.
Understanding and Preparing Industrial Data
- Types of industrial data: time-series, tabular, image, text.
- Data acquisition, cleaning, and preprocessing.
- Exploratory data analysis with Pandas and Matplotlib.
Model Selection and Prototyping
- Choosing between regression, classification, clustering, or anomaly detection.
- Training and evaluating models with Scikit-learn.
- Using TensorFlow or PyTorch for advanced modeling.
Visualizing and Interpreting Results
- Creating 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 and improving models.
- Strategies for integration with operations.
Capstone Project Development
- Finalizing and testing team prototypes.
- Peer review and collaborative debugging.
- Preparing project presentation and technical summary.
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
- Understanding of manufacturing or industrial processes.
- Experience with Python and foundational machine learning concepts.
- Ability to work with both structured and unstructured data.
Audience
- Cross-functional teams.
- Engineers.
- Data scientists.
- IT professionals.
21 Hours
