Course Modules:

Module 1: Introduction to AI in Industrial Settings

• AI vs ML vs Deep Learning
• Role of AI in Industry 4.0
• Use Cases Across Manufacturing
• Overview of Toolkits (Python, KNIME, TensorFlow)

Module 2: Data Preparation and Feature Engineering

• Cleaning and Structuring Industrial Data
• Feature Selection Techniques
• Data Imbalance and Anomaly Handling
• Workshop: Building a Training Dataset

Module 3: Predictive Modeling and Machine Learning

• Regression and Classification in Manufacturing
• Training and Validation
• Predicting Quality, Downtime, and Failure
• Case Study: Predictive Maintenance Model

Module 4: Optimization Techniques

• Linear and Nonlinear Optimization
• Scheduling, Inventory, and Production Planning
• Constraint Handling and Cost Minimization
• Interactive Lab: Optimizing Production Schedule

Module 5: AI in Real-Time Operations

• Streaming Data and Real-Time Decisions
• Edge Deployment of Models
• Digital Twins and AI Integration
• Dashboarding and Visualization Tools

Module 6: Deep Learning Applications

• CNNs for Visual Inspection
• LSTM for Predictive Sequences
• Hands-On: Build a Visual Defect Detection Model
• Limitations of Deep Learning in Industry

Module 7: AI Governance and Ethics

• Explainability of AI Models
• Bias, Privacy, and Model Drift
• Regulatory and Compliance Considerations
• Best Practices for Ethical Deployment

Module 8: Scaling and Integration of AI

• Model Deployment Lifecycle
• Infrastructure: On-Premise vs Cloud vs Edge
• Integration with MES and ERP Systems
• Success Stories in AI Scale-Up

Module 9: Measuring ROI and Business Impact

• Key KPIs for AI in Manufacturing
• Cost-Saving vs Revenue-Generating Projects
• Reporting and Performance Tracking
• Stakeholder Buy-In and Business Cases

Module 10: Final Project and Review

• Group Capstone Project: End-to-End AI Solution
• Presentation to Panel
• Peer Review and Model Feedback
• Course Wrap-Up and Certification