Industrial Applications of AI from Coursera

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Syllabus

Real-time Applications of ML - A Structured Approach and Demos

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

ML Algorithms and Scope for Edge Computing in Electrical Engineering Applications

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

ML Algorithms and Scope for Edge Computing in Mechanical Engineering Applications

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

ML Algorithms and Scope for Edge Computing in Civil Engineering Applications

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

ML algorithms and Scope for Edge Computing in Future

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

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Know what's good

, what to watch for

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Covers data processing, modeling, prediction, and big data, which is standard in engineering

Features Subject Matter Experts as instructors, which ensures quality content

Focuses on practical applications, offering hands-on experience

Provides real-world examples of AI applications in healthcare, illuminating potential solutions

Examines the role of AI in edge computing for various engineering disciplines, bridging theory and practice

Emphasizes core AI principles for challenging real-time applications, catering to advanced learners

Career center

Learners who complete Industrial Applications of AI will develop knowledge and skills that may be useful to these careers:

AI Engineer

AI Engineers design, develop, and deploy AI systems. They have a strong understanding of the AI lifecycle and the ability to apply AI algorithms to real-world problems. This course will help build a foundation in the key AI principles required for challenging real-time applications and provide hands-on experience applying AI algorithms into potential applications.

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Electrical Engineer

Electrical Engineers design, develop, and maintain electrical systems. They have a strong understanding of electrical engineering principles and the ability to apply them to real-world problems. This course will help build a foundation in the ML algorithms and techniques used in electrical engineering, and provide hands-on experience applying these algorithms and techniques to real-world electrical engineering problems.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy ML models to solve business problems. They have a strong understanding of the ML lifecycle and the ability to apply ML algorithms to real-world data. This course will help build a foundation in the ML algorithms such as SVM, KNN, K-means, BERT, Random forest classifier, CNN and Mobile Net V2, and provide hands-on experience applying these algorithms in potential applications.

See salaries and explore the career path for Machine Learning Engineer

Civil Engineer

Civil Engineers design, develop, and maintain civil infrastructure. They have a strong understanding of civil engineering principles and the ability to apply them to real-world problems. This course will help build a foundation in the ML algorithms and techniques used in civil engineering, and provide hands-on experience applying these algorithms and techniques to real-world civil engineering problems.

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Mechanical Engineer

Mechanical Engineers design, develop, and maintain mechanical systems. They have a strong understanding of mechanical engineering principles and the ability to apply them to real-world problems. This course will help build a foundation in the ML algorithms and techniques used in mechanical engineering, and provide hands-on experience applying these algorithms and techniques to real-world mechanical engineering problems.

See salaries and explore the career path for Mechanical Engineer

Data Scientist

Data Scientists build and maintain systems that collect, clean, and analyze data. They create and improve data models that make predictions and identify trends for businesses. Having a foundation in the principles of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) would be beneficial. This course covers the key AI principles required for challenging real-time applications of electrical engineering and provides hands-on experience applying AI algorithms into potential applications.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations and improve business decisions. This course will help build a foundation in the ML algorithms and techniques used in data analysis, and provide hands-on experience applying these algorithms and techniques to real-world data.

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Business Analyst

Business Analysts help businesses understand their data and make better decisions. They use their knowledge of data analysis and business processes to identify opportunities for improvement. This course will help build a foundation in the ML algorithms and techniques used in business analysis, and provide hands-on experience applying these algorithms and techniques to real-world business problems.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They have a strong understanding of software engineering principles and the ability to apply them to real-world problems. This course may be useful for Software Engineers who want to learn more about AI and ML, and how to apply these technologies to their work.

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Project Manager

Project Managers plan, execute, and close projects. They have a strong understanding of project management principles and the ability to apply them to real-world projects. This course may be useful for Project Managers who want to learn more about AI and ML, and how to apply these technologies to their projects.

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Product Manager

Product Managers plan, develop, and launch products. They have a strong understanding of product management principles and the ability to apply them to real-world products. This course may be useful for Product Managers who want to learn more about AI and ML, and how to apply these technologies to their products.

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Consultant

Consultants provide advice and guidance to businesses and organizations. They have a strong understanding of business principles and the ability to apply them to real-world problems. This course may be useful for Consultants who want to learn more about AI and ML, and how to apply these technologies to their work.

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Teacher

Teachers plan, develop, and deliver lessons to students. They have a strong understanding of teaching principles and the ability to apply them to real-world classrooms. This course may be useful for Teachers who want to learn more about AI and ML, and how to apply these technologies to their teaching.

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Researcher

Researchers conduct research to develop new knowledge and understanding. They have a strong understanding of research principles and the ability to apply them to real-world problems. This course may be useful for Researchers who want to learn more about AI and ML, and how to apply these technologies to their research.

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Industrial Applications of AI

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