AI/ML in to Manufacturing: Fundamental knowledge and Practical skills
COURSE BRIEF
This two-day course provides a comprehensive introduction to the fundamental concepts and practical application of Artificial Intelligence (AI) and Machine Learning (ML) in engineering and manufacturing environments. The training equips participants with working knowledge of AI/ML principles, tools, and techniques that can be applied to enhance productivity, efficiency, and innovation.
The course covers the complete AI/ML development lifecycle, including defining and preparing datasets, selecting appropriate machine learning algorithms, data preprocessing, Python-based implementation, model training, evaluation, and deployment. Participants will gain a clear understanding of how AI/ML models are developed and executed in real-world industrial contexts.
A strong emphasis is placed on hands-on learning through practical case studies drawn from design, engineering, and manufacturing applications. In addition, participants will learn how to effectively leverage currently available AI tools and platforms to accelerate development and support data-driven decision-making.
COURSE OUTCOMES AND OBJECTIVES
On successful completion of this short course, you will be able to:
1. Understand the overall Industry 4.0 enabling technologies.
2. Evaluate and critically assess current and future level of digital technologies practices in an organisation.
3. Learn how AL/ML could enhance innovation in your organisation.
4. Understand the full process of developing AI/ML applications.
5. Gaining practical knowledge of preparing dataset, selecting the right ML algorithm, data processing, and execution.
6. Deep understanding of starting AI/ML applications from open software sources.
7. Appreciation of the true use of the currently available AI tools and the mis-conceptual associated with them.
8. Introduce digital technologies in your organisation.
Who should attend?
- This course is designed for both technical professionals and decision-makers in manufacturing organisations who want to understand and apply Artificial Intelligence (AI) and Machine Learning (ML) to improve operational performance, quality, and innovation. It is suitable for engineers, managers, and leaders involved in design, engineering, production, maintenance, quality, and digital transformation initiatives.
- Typical participants include manufacturing, industrial, process, and automation engineers; operations and production managers; quality and reliability engineers; maintenance and asset management professionals; IT and OT specialists; and digital transformation or innovation leads.
- The course is accessible to participants from a wide range of backgrounds, and no prior experience in AI, ML, or programming is required.
Course Details
II. Payment is advanced.
Course Content
- AI
- ML
- Deep Learning – Neural Network
- Large Language Model
- Current AI tools – leverage use and mis-conceptual
- ML application design/engineering
- ML application in manufacturing – machining, planning, maintenance, or X
- Dataset for the ML application design/engineering
- Dataset for the ML application in manufacturing
- Present the most comment ML algorithms.
- Criteria to select the correct algorithms.
- Obtaining ML Algorithm from net.
- Selecting the right algorithm for ML application design/engineering
- Selecting the right algorithm for ML application in manufacturing
- Data processing of ML application design/engineering
- Data processing of ML application in manufacturing
- The python code for the ML application design/engineering
- The python for ML application in manufacturing
- Training and evaluating the ML application in design/engineering.
- Training and evaluating the ML application in manufacturing.
- Reinforced learning of ML application in design/engineering
- Reinforced learning of ML application in manufacturing