Machine Learning & Data Analysis
A Comprehensive 10-Hour Training
This intensive course is designed for rapid, high-impact learning with a focus on hands-on, practical implementation. It equips manufacturing professionals with the tools, techniques, and real-world context to apply machine learning meaningfully in their work.
Topics include:
– Data Handling: ETL, normalization, feature engineering, warehousing
– Exploratory Data Analysis: statistics, trends, root cause detection
– Supervised Learning: regression, classification (e.g., defect detection, quality grading)
– Unsupervised Learning: clustering, dimensionality reduction (e.g., machine state grouping, anomaly detection)
– Ensemble Methods: Random Forests, Gradient Boosting (e.g., predictive maintenance)
– Deep Learning: neural networks, CNNs, DNNs (e.g., visual inspection)
– Advanced Topics: LSTM, reinforcement learning, time series modeling
– Deployment: real-time dashboards, APIs
However, our courses are not academic trainings – the goal is to make participants job-ready, able to collaborate across teams, speak the language of operations, and deploy solutions that deliver impact. To that end, all concepts are taught through audience-specific case studies rooted in real production environments:
For Industry Professionals (engineers, operators, quality managers), we focus, for instance, on process performance – using real sensor data to cluster defect types, detect production drift, or optimize OEE.
For IT/OT Generalists, we embed ML in system architectures – for example, using unsupervised learning to identify anomalous sensor behavior from SCADA logs, then integrating the model into MES workflows via API or OPC-UA.
This context-first approach adds complexity up front, but ensures participants leave not only with technical knowledge, but with the confidence and ability to apply it directly in their work.
Python for Data Science
A Guide for IT/OT Generalists in Manufacturing
This specialized 10-hour course For Python Beginners focuses on essential Python skills for data science in the manufacturing sector.
Topics include:
– Python environments, structure, and workflows
– Learning Python with AI support
– Characteristic code patterns
– Core libraries and use cases:
+ pandas, sqlalchemy, requests for structured data access
+ pandas, numpy for cleaning and transformation
+ pandas, matplotlib, seaborn, scipy.stats for statistical analysis
+ matplotlib, seaborn, plotly for visualization
+ TensorFlow, Keras, PyTorch for deep learning and anomaly detection
+ Flask, FastAPI for lightweight web interfaces and model deployment
