By the end of this module, learners will be able to: Apply ML Algorithm in various aspects of electrical engineering, such as load prediction and feature extraction in substations; Analyze the CNN based tasks related to substation analysis, infrastructure management, and infrared fault image diagnosis

By the end of this module, learners will be able to: Understand the impact of ML in the oil and gas industry; Interpret seismic data processing techniques, with a focus on salt body delineation using CNN; Demonstrate the process of geomodelling based on the Gaussian process regression algorithm; Examine AI applications in the upstream sector of the oil and gas industry; Infer the Service-Oriented Architecture (SOA) of big data for the oil and gas industry

By the end of this module, learners will be able to: Understand a generic ML modeling framework for civil engineering applications; Apply deep learning techniques in construction sites, with a focus on recycled cement strength prediction; Analyze the diverse ML application areas such as transport engineering, road traffic prediction, naval architecture, and wave height forecasting, using deep learning algorithms like ANN, CNN, and YOLO architecture

By the end of this module, learners will be able to: Understand the impact of AI in education; Interpret open-source AI software libraries such as H2O, ImageAI, OpenAI Gym, Keras, TensorFlow, PyTorch, and Scikit-learn; Demonstrate computer vision techniques for car object detection using YOLO; Infer the language and language reasoning in AI with an application of language identification in text; Investigate AI-based speech recognition technology in the healthcare sector for heart disease prediction; Explain policies and strategies related to AI adoption and implementation