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-Based Use Case Selection
- Overview of AI applications in industrial settings
- Categories of use cases: quality assurance, maintenance, energy efficiency, and logistics
- Team assembly and scoping of project objectives
Understanding and Preparing Industrial Data
- Types of industrial data: time-series, tabular, image, and text formats
- Data acquisition, cleaning, and preprocessing techniques
- Exploratory data analysis utilizing Pandas and Matplotlib
Model Selection and Prototyping
- Selecting appropriate methods: regression, classification, clustering, or anomaly detection
- Training and evaluating models using Scikit-learn
- Leveraging TensorFlow or PyTorch for advanced modeling tasks
Visualizing and Interpreting Results
- Designing intuitive dashboards or reports
- Interpreting performance metrics such as accuracy, precision, and recall
- Documenting underlying assumptions and limitations
Deployment Simulation and Feedback
- Simulating edge and cloud deployment scenarios
- Collecting feedback and refining models
- Strategies for integrating solutions into existing operations
Capstone Project Development
- Finalizing and testing team prototypes
- Peer review and collaborative debugging processes
- Preparing project presentations and technical summaries
Team Presentations and Wrap-Up
- Presenting AI solution concepts and final outcomes
- Group reflection and key lessons learned
- Roadmap for scaling use cases within the organization
Summary and Next Steps
Requirements
- Familiarity with manufacturing or industrial processes
- 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 professionals
21 Hours
