Course
Overview
In today’s engineering environments, vast amounts of data are generated from sensors, systems,
and operational processes, but turning that data into actionable insights remains a challenge.
This course provides a practical introduction to Machine Learning (ML) tailored specifically for
engineering applications. Participants will learn how ML can be used to support predictive
maintenance, fault detection, quality control, and system optimization.
Through guided demonstrations and real-world case studies, participants will gain the ability to
understand, interpret, and propose ML-driven solutions withing their own engineering domains.
Training
Objectives
By the end of this course, participants will be able to:
- Classify engineering problems into appropriate ML types
- Map the machine learning workflow to real engineering use cases
- Analyze engineering datasets to identify issues such as missing values and outliers
- Interpret basic machine learning outputs and visualizations
- Propose a practical ML solution for an engineering problem
Target
Audience
This course is designed for:
- Engineers (mechanical, electrical, manufacturing, software)
- Technical professionals working with data or systems
- Maintenance and operations personnel
- Professionals involved in process improvement or system optimization
Prerequisites
- Basic programming knowledge (e.g., Python, MATLAB, or similar)
- Familiarity with engineering data (e.g., sensor data, logs)
- No prior Machine Learning experience required
Teaching
Methodology
- Instructor-led sessions
- Interactive discussions
- Hands-on guided demonstration
- Group activities and case study analysis
- Practical, engineering-focused examples
