Industrial Applications of AI

This course is part of Intelligent Digital Factories Specialization

Instructor: Subject Matter Expert

Access provided by ExxonMobil

2,156 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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This course is part of the Intelligent Digital Factories Specialization

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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