AI-Driven Digital Twin Specialist
This role focuses on creating and managing digital twins of WEEE components using AI and computer vision, integrating inspection data to optimize the disassembly and recycling processes.
Digital Twin Technology
Expertise in creating and maintaining digital twins of various electronic components
Computer Vision
Proficiency in using computer vision to accurately identify and describe subsystems and modules.
MathWorks Computer Vision Engineer Professional Certificate
Target Group
Mid Level Employee
Level
Foundations
AI-Driven Digital Twin Specialist MathWorks Computer Vision Engineer Professional Certificate
Prepare for a career in the rapidly expanding field of computer vision. The ability to extract meaningful information from visual data is crucial for efficiently developing smart monitoring systems, enhancing medical diagnostics, and powering the next generation of autonomous vehicles. This program is designed to transform those with little or no prior image data experience into proficient computer vision practitioners by completing several hands-on projects with real-world applications. By the end of the program, you will have the skills necessary to automate image processing tasks, detect and track motion, train deep learning models for image classification and object detection tasks, and implement advanced deep learning techniques like data augmentation and AI-assisted annotation. Possessing these skills will prepare you to keep pace with modern systems, which increasingly incorporate cameras into their designs. Throughout the program, you will use MATLAB, an industry-standard platform known for its user-friendly interface and robust functions that simplify complex algorithms. The intuitive apps and functions mean that you don't need to be a programmer or math expert to achieve success. You will leverage MATLAB capabilities to rapidly prototype and iterate on solutions, giving you a competitive edge in the job market and the confidence to push the boundaries of what's possible in computer vision and deep learning.
Provider
www.coursera.org
www.coursera.org
Target
- Individuals seeking a career change or advancement in tech
- Entry-level professionals and students in STEM fields
- Engineers and data scientists interested in computer vision
- Medical professionals looking to enhance diagnostic capabilities through technology
- Researchers in environmental science and autonomous systems
Sector
- Automotive industry (autonomous vehicles)
- Healthcare and medical imaging
- Environmental science
- Robotics and automation
- Education and accessibility technology
Area
- Computer vision and image processing
- Deep learning and artificial intelligence
- Data analysis and interpretation
- Automated monitoring systems
- Real-world project implementation and prototyping
Method
Online
Online
Certification
Yes
Yes
Duration
2 months at 10 hours a week
2 months at 10 hours a week
Assessment
No
No
Cost
Free
Free
Learning Outcomes
- Automatically extract information from images
- Detect and track objects in images and videos
- Apply the full deep-learning workflow to computer vision challenges
- Export models to common formats like Tensorflow or PyTorch
Learning Content
- Introduction to Image Processing
- Perform analysis on a variety of common image datatypes
- Detect objects and regions of interest using segmentation
- Complete a project analyzing Antarctic ice melt in satellite images
- Image Segmentation, Filtering, and Region Analysis
- Use segmentation to detect and analyze regions of interest
- Segment and analyze 3D images, such as MRI images
- Use interactive tools to test segmentation approaches
- Automating Image Processing
- Apply image processing algorithms to large datasets
- Analyze video files and calculate statistics
- Complete a specialization-level project detecting cars in noisy videos
- Introduction to Computer Vision
- Use algorithms for feature detection, extraction, and matching
- Perform image registration and stitching
- Complete a project stitching images from NASA’s Mars Rover
- Machine Learning for Computer Vision
- Train and evaluate models for image classification
- Train object detection machine learning models
- Customize model training using cost matrices
- Object Tracking and Motion Detection
- Use pre-trained neural networks like YOLO for object detection
- Detect motion and perform multi-object tracking
- Introduction to Deep Learning for Computer Vision
- Retrain common models like GoogLeNet and ResNet
- Investigate model behavior and improve performance
- Complete a real-world project practicing the deep learning workflow
- Deep Learning for Object Detection
- Retrain YOLO models for specific applications
- Evaluate models by examining class and location accuracy
- Advanced Deep Learning Techniques
- Train specialized anomaly detection models
- Generate synthetic training images
- Use AI-assisted auto-labeling to save time and effort
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AI-Driven Digital Twin Specialist Digital Twins
In this course, learners will be introduced to the concept of Digital Twins, learn how it is applied in manufacturing, and what businesses should consider as they decide to implement this technology. Considerations include information technology infrastructure, the business value of implementing Digital Twins, and what needs to happen across the organization to ensure successful implementation. Learners will hear from industry experts as they share their perspectives on the opportunities and challenges of implementing Digital Twins, how Digital Twins is being implemented in their companies, and insights on the future of this technology within their industry and across manufacturing. The content presented in this course draws on a number of real-life interviews and case studies, and was created through a partnership with Siemens.
Provider
www.coursera.org
www.coursera.org
Target
- Manufacturing professionals
- IT infrastructure managers
- Business analysts
- Operations managers
- Technology adoption strategists
- Executives and decision-makers in manufacturing
Sector
- Manufacturing industry
- Information technology
- Industrial engineering
Area
- Digital transformation
- Technology implementation
- Operational efficiency
- Business strategy and value analysis
Method
Online
Online
Certification
Yes
Yes
Duration
9 hours to complete / 3 weeks at 3 hours a week
9 hours to complete / 3 weeks at 3 hours a week
Assessment
No
No
Cost
Free
Free
Learning Outcomes
- Understand the basics of digital twins, digital twins platform and ecosystem
- Learn the implementation of digital twins in manufacturing, the corresponding business values, and risks
- Get to know the future trends of digital twins and digital threads
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
Learning Content
- What is Digital Twins?
- Learn the basics behind this technology
- Describe the applications and uses for digital twins within a manufacturing setting
- Digital Twins Platform, Ecosystem, and Business Context
- Address the digital twin platform ecosystem
- Understand the business context and advantages of digital twins
- Review risks and challenges surrounding this technology
- Future Trends and Summary
- Learn about the forecast of future trends for digital twins
- Explore the related concept of digital threads
- Work through a case project for your final assessment
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