AI in Mechanical Engineering: 5 Real-World No-Code AI Projects

What Will You Learn?

• Course Objectives:
• • To introduce mechanical engineering students to the concept of integrating AI into their domain without coding.
• • To familiarize students with the RapidMiner platform as a No-Code AI tool for mechanical engineering applications.
• • To provide students with a basic understanding of exploratory data analysis (EDA) concepts and their implementation using RapidMiner.
• • To equip students with the skills necessary to import, clean, preprocess, and analyze mechanical engineering data using RapidMiner.
• • To enable students to implement machine learning algorithms for mechanical engineering data analysis and prediction using RapidMiner.
• • To guide students through end-to-end project implementation, including data import, EDA, data preprocessing, ML algorithm implementation, and performance evaluation.
• Course Outcomes: By the end of the course, learners will be able to:
• • Install and navigate the RapidMiner platform proficiently.
• • Conduct basic exploratory data analysis on mechanical engineering datasets using RapidMiner.
• • Clean and preprocess mechanical engineering data for further analysis and modeling.
• • Implement machine learning algorithms such as regression, classification, and clustering for mechanical engineering data analysis.
• • Evaluate the performance of machine learning models and interpret the results in the context of mechanical engineering applications.
• • Execute end-to-end projects integrating AI into mechanical engineering, from data preprocessing to model deployment.
• • Understand the potential future job opportunities for mechanical engineering professionals skilled in integrating AI technologies.
• • Apply acquired skills and knowledge to contribute effectively to the development and implementation of AI solutions in mechanical engineering.