Industrial Applications of AI
Industrial Applications of AI
This course is part of Intelligent Digital Factories Specialization
Instructor: Subject Matter Expert
2,473 already enrolled
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Recommended experience
16 reviews
Recommended experience
Skills you'll gain
- Machine Learning Software
- Construction Engineering
- Artificial Neural Networks
- Machine Learning Algorithms
- AI Enablement
- Convolutional Neural Networks
- Electrical Engineering
- AI literacy
- Machine Learning
- Applied Machine Learning
- Deep Learning
- Electrical Substation
- Civil Engineering
- Computer Vision
- Electric Power Systems
- Image Analysis
- Artificial Intelligence
Tools you'll learn
Details to know
5 assignments
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There are 5 modules in this course
The course Embarks on a transformative learning journey exploring the power of Artificial Intelligence across diverse fields such as electrical, mechanical, civil, and general applications. This course elevates the learner’s insight on AI towards the real-world practices by bridging the gap between theory and practical applications. It also provides hands-on experience of applying AI algorithms into potential applications. The examples of AI in healthcare provided in the course will enlighten the learners with an end-to-end perspective of real-world solutions. This course is crafted to introduce key AI principles required for challenging real-time applications of electrical engineering like load predictions and fault diagnosis in substations. The course also covers the application of AI in mechanical engineering, encompassing seismic data processing, geo-modelling, and reservoir engineering. The civil engineering learners will learn about AI's role in cloud data collection at construction sites and its applications in transport engineering and road traffic prediction. Immerse yourself in the future of AI with a focus on Machine and Deep learning operations, gaining insights that enable you to distinguish and apply AI based solutions to real-world challenges. Explore hands-on exercises with software support, gaining a comprehensive understanding of AI metrics. Enhance your skills and broaden your horizons with the power of AI.
By the end of this module, learners will be able to: Understand the ML algorithms such as SVM, KNN, K-means, BERT, Random forest classifier, CNN and Mobile Net V2; Apply ML techniques in diverse real-time applications such as automated vehicle support, fraud system diagnosis, and shop floor management, neural networks for ground water quality analysis, diabetic retinopathy, image classification in IoT, forest fire detection and remotely piloted aircraft case studies
What's included
20 videos2 readings1 assignment1 discussion prompt
20 videos•Total 133 minutes
- About the Specialization•6 minutes
- About the Course•4 minutes
- Machine Learning(ML) Fundamentals and Principles - PART I•6 minutes
- Machine Learning(ML) Fundamentals and Principles - PART II•5 minutes
- Automated Vehicle Support using ML - PART I•5 minutes
- Automated Vehicle Support using ML - PART II•6 minutes
- Fraud System Diagnosis using ML - PART I•5 minutes
- Fraud System Diagnosis using ML - PART II•7 minutes
- Deep Learning-based Shop Floor Management - PART I•4 minutes
- Deep Learning-based Shop Floor Management - PART II•9 minutes
- Neural Networks-based Ground Water Quality Distribution Analysis•8 minutes
- Potential Applications of AI in Healthcare - Discussion - PART I•4 minutes
- Potential Applications of AI in Healthcare - Discussion - PART II•12 minutes
- Image Classification in IoT Devices - Case Study - PART I•8 minutes
- Image Classification in IoT Devices - Case Study - PART II•6 minutes
- Remotely Piloted Aircraft - Case Study - PART I•5 minutes
- Remotely Piloted Aircraft - Case Study - PART II•8 minutes
- AI Products - A survey•8 minutes
- Education Quality updates in Design, Development and Delivery using ML - PART I•7 minutes
- Education Quality updates in Design, Development and Delivery using ML - PART II•9 minutes
2 readings•Total 20 minutes
- Course Reading•10 minutes
- Course Glossary•10 minutes
1 assignment•Total 30 minutes
- Assessment on Real-time Applications of ML - A Structured Approach and Demos•30 minutes
1 discussion prompt•Total 10 minutes
- Statement: Considering the significant role that AI now plays in various aspects of our world and its increasing importance in different professions, there are new job roles coming up for the human. In contradiction, the International Monetary Fund (IMF) states, 'AI likely to worsen economic inequality' How do you envision the integration of Artificial Intelligence impacting the future of human? Drop your opinion•10 minutes
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
What's included
22 videos1 assignment
22 videos•Total 191 minutes
- ANN Architecture for Substations - PART I•11 minutes
- ANN Architecture for Substations - PART II•10 minutes
- ANN Architecture for Substations - PART III•17 minutes
- Load Prediction in Substations - A Case Study - PART I•4 minutes
- Load Prediction in Substations - A Case Study - PART II•9 minutes
- Geo-spatial Database for Power Infrastructure - PART I•10 minutes
- Geo-spatial Database for Power Infrastructure - PART II•6 minutes
- Geo-spatial Database for Power Infrastructure - PART III•10 minutes
- Feature Extraction of Substation using Deep Learning - A Case study - PART I•10 minutes
- Feature Extraction of Substation using Deep Learning - A Case study - PART II•11 minutes
- Feature Extraction of Substation using Deep Learning - A Case study - PART III•3 minutes
- Power Grid Stability and Secondary Substation Model - PART I•8 minutes
- Power Grid Stability and Secondary Substation Model - PART II•9 minutes
- Power Grid Stability and Secondary Substation Model - PART III•7 minutes
- Estimation of Unknown Secondary Substation Profile - A Case Study•7 minutes
- Characterization of Substation Site Features - Practisioner Approach•13 minutes
- CNN-based Preliminary Siting of Substation - A Case Study•7 minutes
- Substation Device Diagnosis using Unsupervised ML Algorithm - PART I•7 minutes
- Substation Device Diagnosis using Unsupervised ML Algorithm - PART II•8 minutes
- CNN-based Infrared Fault Image Diagnosis - A Case Study - PART I•9 minutes
- CNN-based Infrared Fault Image Diagnosis - A Case Study - PART II•6 minutes
- CNN-based Infrared Fault Image Diagnosis - A Case Study - PART III•9 minutes
1 assignment•Total 30 minutes
- Assessment on ML Algorithms and Scope for Edge Computing in Electrical Engineering Applications•30 minutes
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
What's included
18 videos1 assignment
18 videos•Total 141 minutes
- Impact of ML in O&G Industry - A Review - PART I•10 minutes
- Impact of ML in O&G Industry - A Review - PART II•13 minutes
- Seismic Data Processing Techniques(Salt Body Delienation) - PART I•8 minutes
- Seismic Data Processing Techniques(Salt Body Delienation) - PART II•14 minutes
- Geomodeling Process•9 minutes
- ML in Reservoir Engineering(Reservoir Rock Classification) - PART I•7 minutes
- ML in Reservoir Engineering(Reservoir Rock Classification) - PART II•10 minutes
- Optimal Production Engineering in O&G Industry - PART I•7 minutes
- Optimal Production Engineering in O&G Industry - PART II•5 minutes
- AI in Upstream Sector of O&G Industry - PART I•2 minutes
- AI in Upstream Sector of O&G Industry - PART II•10 minutes
- Advances in AI Technology for O&G Industry - PART I•6 minutes
- Advances in AI Technology for O&G Industry - PART II•8 minutes
- Fundamentals of Data Handling in O&G Industry - PART I•7 minutes
- Fundamentals of Data Handling in O&G Industry - PART II•9 minutes
- Fundamentals of Data Handling in O&G Industry - PART III•4 minutes
- SOA of Big Data for O&G Industry - PART I•5 minutes
- SOA of Big Data for O&G Industry - PART II•8 minutes
1 assignment•Total 30 minutes
- Assessment on ML Algorithms and Scope for Edge Computing in Mechanical Engineering Applications•30 minutes
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
What's included
22 videos1 assignment
22 videos•Total 185 minutes
- ML for Civil Engineering - PART I•8 minutes
- ML for Civil Engineering - PART II•8 minutes
- ML for Civil Engineering - PART III•5 minutes
- ML for Civil Engineering - PART IV•10 minutes
- Cloud Data collection about the Construction Site•19 minutes
- Generic ML Modelling Framework for Civil Engineering Applications - PART I•8 minutes
- Generic ML Modelling Framework for Civil Engineering Applications - PART II•10 minutes
- Generic ML Modelling Framework for Civil Engineering Applications - PART III•7 minutes
- Deep Learning Techniques in Construction Industry - PART I•7 minutes
- Deep Learning Techniques in Construction Industry - PART II•12 minutes
- ML Approach for Construction Management - PART I•11 minutes
- ML Approach for Construction Management - PART II•7 minutes
- CNN Based Planetary Lego Brick - PART I•7 minutes
- CNN Based Planetary Lego Brick - PART II•10 minutes
- CNN Based Planetary Lego Brick - PART III•4 minutes
- AI in Transport Engineering - A Survey•8 minutes
- Road Traffic Prediction - Bayesian Approach•7 minutes
- ML for Naval Architecture - PART I•9 minutes
- ML for Naval Architecture - PART II•8 minutes
- ML for Naval Architecture - PART III•3 minutes
- ML for Naval Architecture - PART IV•12 minutes
- AI Based Wave Height Forecasting - A Case Study•6 minutes
1 assignment•Total 30 minutes
- Assessment on ML Algorithms and Scope for Edge Computing in Civil Engineering Applications•30 minutes
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
What's included
19 videos1 assignment
19 videos•Total 147 minutes
- AI Impacts in Education - PART I•9 minutes
- AI Impacts in Education - PART II•9 minutes
- AI Open Source Software Libraries - PART I•7 minutes
- Computer Vision (Image and PDF) - PART I•8 minutes
- Computer Vision (Image and PDF) - PART II•9 minutes
- Computer Vision (Image and PDF) - PART III•8 minutes
- Computer Vision (Image and PDF) - PART IV•7 minutes
- Language and Language Reasoning - PART I•8 minutes
- Language and Language Reasoning - PART II•8 minutes
- Speech Recognition - PART I•4 minutes
- Speech Recognition - PART II•14 minutes
- Healthcare and Biology - PART I•8 minutes
- Healthcare and Biology - PART II•7 minutes
- Ethical Challenges in AI - PART I•7 minutes
- Ethical Challenges in AI - PART II•4 minutes
- Economy Implications due to AI - PART I•6 minutes
- Economy Implications due to AI - PART II•9 minutes
- Economy Implications due to AI - PART III•4 minutes
- Policies and Strategies for AI•10 minutes
1 assignment•Total 30 minutes
- Assessment on ML algorithms and Scope for Edge Computing in Future •30 minutes
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Reviewed on Sep 28, 2024
It was very informative and helpful for me and the new technology I can use in construction and other industries
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