Type of course:
Digital learning, Lesson
Language:
EN
Duration:
15 minutes
Proficiency:
Beginner
Target:
Manager, Professionals, Workers
SUMMARY
What makes a data science project impactful in manufacturing? Success hinges on evaluating and refining models to meet real-world demands. This lesson explores the critical phase of model evaluation, ensuring that your solutions are not only functional but exceptional.
Discover essential performance metrics, learn how to balance overfitting and underfitting, and explore techniques for aligning models with business goals. From addressing data quality issues to ensuring model generalization, this lesson equips you to refine and validate analytical solutions.
Through practical examples and hands-on activities, you will develop a robust framework for measuring model success and improving outcomes.
By the end of this lesson, you will be ready to evaluate your work with precision, delivering actionable insights that drive meaningful improvements in manufacturing operations.
Are you prepared to achieve exceptional results? Let’s get started!
About The Author
Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.
Learning outcomes
- By the end of this lesson, learners will be able to explain the purpose and key steps of the Evaluation phase in CRISP-DM (Cross Industry Standard Process for Data Mining).
- By the end of this lesson, learners will be able to list most common metrics used to evaluate model performance in manufacturing.
- By the end of this lesson, learners will be able to differentiate between overfitting and underfitting, and explore strategies to mitigate them.
Course Content
Topics
Digital Transformation, Machine Learning, Artificial Intelligence (AI)
Provided by
-
University of Groningen
+23
enrolled
Related
-
IntermediateAugmenting Workers
By Faculty of Engineering of University of PortoThis lesson discusses technologies that enhance human capabilities in the workplace, such as exoskeletons and augmented reality, empowering workers to perform complex tasks more efficiently and safely.
Digital learning, Lesson- Quiz1
-
IntermediateData Analytics for Energy Efficiency and Sustainability
By Faculty of Engineering of University of PortoThis lesson focuses on using real-time data analytics to optimize energy usage in industrial settings, reducing waste, and improving sustainability through smart energy management systems.
Digital learning, Lesson- Quiz1
-
IntermediateAugmented Reality Application Development for Equipment Health Monitoring
By Laboratory for Manufacturing Systems and Automation of Patras - Laboratory for Manufacturing Systems and Automation of PatrasThis lesson teaches learners how to design Augmented Reality (AR) applications for monitoring equipment health. They will learn to formulate microservices for AR-based monitoring, create data monitoring and visualization workflows, and develop AR modules that provide real-time monitoring of equipment performance.
Digital learning, Lesson -
IntermediateIntroduction to MQTT
By Beia Consult InternationalMessage Queuing Telemetry Transport (MQTT) is a lightweight messaging protocol for Internet of Things (IoT), using a publish/subscribe model for efficient, real-time communication. Common uses include smart homes, industrial automation, and environmental monitoring.
Digital learning, Lesson- Quiz1
-
IntermediateIntroduction to Time Series Databases
By Beia Consult InternationalTime Series Databases (TSDBs) efficiently store and retrieve time-stamped data for applications like IoT, monitoring, and finance. They come in SQL-based (e.g., MySQL, PostgreSQL) for structured queries and NoSQL-based (e.g., InfluxDB, MongoDB) for scalability and high-velocity data handling. The choice depends on data volume, query complexity, and scalability needs.
Digital learning, Lesson- Quiz1
-
IntermediateIntroduction to Data Visualisation
By Beia Consult InternationalData visualization simplifies complex data through graphs, charts, and dashboards, enabling quick insights for decision-making. Grafana, a powerful open-source tool, supports real-time monitoring, interactive dashboards, and multi-source data integration for IoT, business intelligence, and analytics.
Digital learning, Lesson- Quiz1
-
IntermediateHow Grafana Is Used in IoT Applications
By Beia Consult InternationalThis course explores how Grafana enhances IoT monitoring with real-time dashboards. Learn to integrate data sources, configure visualizations, and set up alerts for insights in smart homes, industrial IoT, and environmental monitoring.
Digital learning, Lesson- Quiz1
-
BeginnerTrack and Trace with Radio Frequency Identification
By JOANNEUM RESEARCHThis learning path is designed to provide students with an introduction to the concepts of Radio Frequency Identification technology for use in track and trace applications. This includes detailed explanations and descriptions of RFID readers, tags and Near Field Communication (NFC) technology.
Digital learning, Path- Course Certificate
49 € -
BeginnerNear Field Communication
By JOANNEUM RESEARCHThis lesson explains Near Field Communication (NFC). NFC is a technology that allows you to interact securely with the world around you by simply touching it. NFC wireless technology is found in billions of smartphones, tablets, consumer and industrial electronics.
Digital learning, Lesson- Quiz1
-
BeginnerRadio Frequency Identification Tags
By JOANNEUM RESEARCHIn this lesson, we will describe Radio Frequency Identification (RFID) tags. RFID tags are small, wireless devices that use radio waves to identify and track objects. They consist of a microchip and an antenna, and can be attached to or embedded in a variety of objects to enable automated data collection and management. There are many different RFID tags to choose from, depending on the application and environmental factors.
Digital learning, Lesson- Quiz1
-
Beginner
Introduction in Radio Frequency Identification Technology
By JOANNEUM RESEARCHThe learning material starts with asset tracking with Radio Frequency Identification (RFID). Asset tracking is an increasingly important key technology for industrial manufacturing companies. Digital asset tracking brings greater transparency to the production process, allowing you to see the progress and status of your production line at any time. In this learning resource, we explain exactly what the concept means and what the benefits are for companies.In this lesson, we will introduce the basic key concepts of RFID technology.
Digital learning, Lesson- Quiz1
-
IntermediateRadio Frequency Identification Readers
By JOANNEUM RESEARCHIn this lesson, we will describe Radio Frequency Identifier (RFID) readers. RFID readers are devices that use radio waves to identify and track tags attached to objects. The reader sends a signal that activates the tag, which then sends its unique identification information back to the reader wirelessly.
Digital learning, Lesson- Quiz1