Curriculum
- 20 Sections
- 64 Lessons
- 20 Weeks
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- Introduction to Industrial Analytics8
- 1.1Industry 4.0 & the Role of AI/ML
- 1.2Assessment – Industry 4.0 & the Role of AI/ML10 Questions
- 1.3Industrial Use Cases: Quality Inspection, Predictive Maintenance, Yield Optimization
- 1.4Assessment – Industrial Use Cases: Quality Inspection, Predictive Maintenance, Yield Optimization9 Questions
- 1.5Types of Data: Sensor Data, Log Data, Images, Structured vs Unstructured
- 1.6Assessment – Types of Data: Sensor Data, Log Data, Images, Structured vs Unstructured9 Questions
- 1.7Overview of SCADA, MES, UNS, JSON-based Data Exchange
- 1.8Assessment – Overview of SCADA, MES, UNS, JSON-based Data Exchange10 Questions
- Python for Data Science & Orange Introduction4
- Data Handling & Preprocessing4
- Exploratory Data Analysis (EDA)3
- ML Concepts via Orange and Python4
- Regression Techniques3
- Classification Models3
- Clustering & Dimensionality Reduction3
- Time-Series Basics3
- Forecasting Methods3
- Predictive Maintenance3
- Anomaly Detection3
- Deep Learning Essentials3
- Industrial Vision with OpenCV3
- CNNs for Quality Inspection3
- Annotation Tools and Synthetic Data3
- AI on Edge3
- MES, SCADA & JSON Integration3
- Unified Namespace (UNS) & Data Pipelines3
- Dashboards & Visual Analytics3