AI-Driven Recycling Specialist
This role focuses on developing and implementing AI and machine learning models to optimize battery disassembly, recycling processes, and state-of-health certification for end-of-life Lithium-ion batteries. Additionally, it involves integrating Digital Product Passports and asset administration shells to enhance traceability, compliance, and efficiency in battery lifecycle management.
Machine Learning and AI Expertise
Proficiency in developing and implementing machine learning models and AI algorithms, particularly for optimization and decision support.
AI-Driven Recycling Specialist/Digital Product Passport Integration DPP Training
In a world that is moving towards digitization and sustainability faster than ever before, it is essential to become familiar with the latest technologies and concepts. One of these key terms is the “digital product passport” — an innovative tool that promotes transparency and sustainability throughout a product's supply chain. It will be introduced successively for individual industries in the European Union from 2027 until it is to apply to all manufacturers in 2030. But what exactly is a digital product passport and why should you and your company start working on it today and know more about it? This is exactly where our trainings and workshops come in!
Provider
PhygiCon
PhygiCon
Target
- Entrepreneurs
- Managers
- IT specialists
- Employees in production, logistics, and sustainability
- Those interested in blockchain and digitization
Sector
- Digitalization
- Sustainability
- Supply Chain Management
Area
- Digital Product
- Product Transparency
- EU Regulations
- Sustainability Compliance
Method
- Workshops
- Case Studies
- Practical Implementation
- Interactive Sessions
Certification
No
No
Duration
15 Hours
15 Hours
Assessment
No
No
Cost
Cost varies as per different options on the website
Cost varies as per different options on the website
Learning Outcomes
- Define and explain the concept of a Digital Product Passport.
- Understand its role in sustainability, transparency, and circular economy.
- Learn how DPPs will be introduced in the EU from 2027 to 2030.
Learning Content
- 1-hour taster course: Introduction to the digital product passport
- 2-hour training: In-depth and practical applications
- 4-hour training: Comprehensive understanding and fields of application
- 8-hour full-day workshop: Intensive training and concrete strategy development
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AI-Driven Recycling Specialist Mastering in Advanced Deep Learning Computer Vision™
1. Introduction to Computer Vision • Overview of Computer Vision and its significance in AI. • Understanding how computers interpret and analyze visual data. 2. Deep Learning Models for Computer Vision • Introduction to Convolutional Neural Networks (CNNs) and their role in Computer Vision. • Key models like AlexNet, VGG, ResNet, and EfficientNet. 3. Image Processing with Deep Learning • Techniques for preprocessing images (e.g., normalization, resizing, augmentation). • Importance of image filtering and transformations. 4. Computer Vision Image Segmentation Explained • Explanation of image segmentation and its use in dividing images into meaningful regions. • Differences between semantic and instance segmentation. 5. Image Features and Detection for Computer Vision • Understanding feature extraction (edges, corners, blobs). • Techniques for feature detection and matching. 6. SIFT (Scale-Invariant Feature Transform) Explained • Explanation of SIFT and its role in identifying key points and matching across images. • Applications of SIFT in image stitching and object recognition. 7. Object Detection in Computer Vision • Key algorithms: YOLO, SSD, Faster R-CNN. • Techniques for detecting objects in real-time. 8. Datasets and Benchmarks in Computer Vision • Overview of popular datasets (e.g., COCO, ImageNet, Open Images). • Importance of benchmarks in evaluating models. 9. Segmentation in Computer Vision • Explanation of segmentation techniques (e.g., region-based and clustering-based methods). • Importance of accurate segmentation for downstream tasks. 10. Supervised Segmentation Methods in Computer Vision • Overview of deep learning methods like U-Net and Mask R-CNN. • Supervised learning approaches for segmentation tasks. 11. Unlocking the Power of Optical Character Recognition (OCR) • Explanation of OCR and its role in text recognition from images. • Applications in document processing, ID verification, and automation. 12. Handwriting Recognition vs. Printed Text • Differences in recognizing handwriting and printed text. • Challenges and deep learning techniques for each. 13. Facial Recognition and Analysis in Computer Vision • Applications of facial recognition (e.g., authentication, surveillance). • Understanding face detection and facial analysis methods. 14. Facial Recognition Algorithms and Techniques • Popular algorithms like Eigenfaces, Fisherfaces, and deep learning models. • Role of embeddings and feature vectors in facial recognition. 15. Camera Models and Calibrations in Computer Vision • Overview of camera models and intrinsic/extrinsic parameters. • Basics of lens distortion and its correction. 16. Camera Calibration Process in Computer Vision • Steps for calibrating a camera and improving image accuracy. • Tools and libraries for camera calibration. 17. Motion Analysis and Tracking in Computer Vision • Techniques for motion detection and object tracking (e.g., optical flow, Kalman filters). • Applications in surveillance and autonomous vehicles. 18. Segmentation and Grouping Moving Objects • Methods for segmenting and grouping moving objects in videos. • Applications in traffic monitoring and video analytics. 19. 3D Vision and Reconstruction in Computer Vision • Introduction to 3D vision and its importance in depth perception. • Methods for reconstructing 3D structures from 2D images. 20. Stereoscopic Vision and Depth Perception in Computer Vision • Explanation of stereoscopic vision and its use in 3D mapping. • Applications in robotics, AR/VR, and 3D modeling. 21. Applications of Computer Vision • Broad applications in healthcare, agriculture, retail, and security. • Real-world examples of AI-driven visual solutions. 22. Applications of Image Segmentation in Computer Vision • Use cases in medical imaging, self-driving cars, and satellite imagery. • How segmentation helps in data analysis and decision-making. 23. Real-Time Case Study Applications of Computer Vision • End-to-end case studies in self-driving cars, facial recognition, and augmented reality. • Practical insights into implementing Computer Vision solutions in real-time scenarios. This comprehensive course ensures that learners gain both theoretical and practical knowledge to excel in Computer Vision, paving the way for exciting opportunities in AI-powered fields.
Provider
Udemy
Udemy
Target
- Aspiring data scientists
- AI and machine learning practitioners
- Computer science students
- Professionals in tech industries
Sector
- Technology
- Artificial Intelligence
- Data Science
Area
- Computer Vision
- Deep Learning
- Image Processing
Method
Online
Online
Certification
Yes
Yes
Duration
5 hours on-demand video
5 hours on-demand video
Assessment
No
No
Cost
€39.99
€39.99
Learning Outcomes
- Introduction to Computer Vision: Understand the core principles and applications of computer vision in AI.
- Deep Learning Models for Computer Vision: Learn how advanced deep learning models revolutionize computer vision tasks.
- Image Processing with Deep Learning: Explore techniques to enhance and analyze images using deep learning methods.
- Computer Vision Image Segmentation Explained: Master the fundamentals of dividing images into meaningful segments for analysis.
- Image Features and Detection for Computer Vision: Discover how to extract and detect key image features to enable AI understanding.
- SIFT (Scale-Invariant Feature Transform) Explained: Learn the mechanics of SIFT for recognizing and matching image features.
- Object Detection in Computer Vision: Develop skills to identify and classify objects within images and video.
- Datasets and Benchmarks in Computer Vision: Explore commonly used datasets and performance benchmarks for computer vision projects.
- Segmentation in Computer Vision: Dive deeper into methods for isolating objects and regions within an image.
- Supervised Segmentation Methods in Computer Vision: Learn how labeled data is used to train models for accurate image segmentation.
- Unlocking the Power of Optical Character Recognition (OCR): Discover how OCR is applied to extract text from images and scanned documents.
- Handwriting Recognition vs. Printed Text: Understand the challenges and techniques for recognizing handwritten and printed text.
- Facial Recognition and Analysis in Computer Vision: Learn how AI recognizes and analyzes human faces for identification and emotion detection.
- Facial Recognition Algorithms and Techniques: Explore state-of-the-art algorithms and approaches used in facial recognition systems.
- Camera Models and Calibrations in Computer Vision: Gain insights into camera parameters and how to model their effects in computer vision.
- Camera Calibration Process in Computer Vision: Master the techniques for calibrating cameras to improve accuracy in vision tasks.
- Motion Analysis and Tracking in Computer Vision: Learn methods for analyzing and tracking object movements within video streams.
- Segmentation and Grouping Moving Objects: Understand how to isolate and group objects in motion for dynamic scene analysis.
- 3D Vision and Reconstruction in Computer Vision: Explore techniques for creating 3D models from 2D images and scenes.
- Stereoscopic Vision and Depth Perception in Computer Vision: Learn how to replicate depth perception using stereoscopic imaging techniques.
- Applications of Computer Vision: Discover the broad spectrum of industries where computer vision creates transformative impact.
- Applications of Image Segmentation in Computer Vision: Explore real-world uses of image segmentation in healthcare, automotive, and more.
- Real-Time Case Study Applications of Computer Vision: Apply your learning to practical, real-time computer vision projects and case studies.
Learning Content
- Introduction
- Deep Learning Models for Computer Vision
- Image Processing with Deep Learning
- Computer Vision Image Segmentation Explained
- Image Features and Detection for Computer Vision
- SIFT (Scale-Invariant Feature Transform) Explained
- Object Detection in Computer Vision
- Datasets and Benchmarks in Computer Vision
- Segmentation in Computer Vision
- Supervised Segmentation Methods in Computer Vision
- Unlocking the Power of Optical Character Recognition (OCR)
- Handwriting Recognition vs. Printed Text
- Facial Recognition and Analysis in Computer Vision
- Facial Recognition Algorithms and Techniques
- Camera Models and Calibrations in Computer Vision
- Camera Calibration Process in Computer Vision
- Motion Analysis and Tracking in Computer Vision
- Segmentation and Grouping Moving Objects
- 3D Vision and Reconstruction in Computer Vision
- Stereoscopic Vision and Depth Perception in Computer Vision
- Applications of Computer Vision
- Applications of Image Segmentation in Computer Vision
- Real-Time Case Study Applications of Computer Vision
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AI-Driven Recycling Specialist/ Algorithm Development AI for Design and Optimization
Artificial intelligence and machine learning are revolutionizing design processes, optimizing strategies, and fostering innovation across industries. This course offers the knowledge to harness AI to enhance design and optimization capabilities, covering generative design, evolutionary algorithms, and topology optimization.
Provider
Coursera
Coursera
Target
- Designers
- Engineers
- Product developers
- Design strategists
- AI enthusiasts
Sector
- Design and creative industries
- Engineering
- Product development
- Architecture
- Technology and innovation
Area
- Artificial Intelligence applications
- Design processes
- Optimization strategies
- Generative design techniques
- Evolutionary algorithms
- Topology optimization
Method
Online
Online
Certification
Yes
Yes
Duration
10 hours to complete/3 weeks at 3 hours a week
10 hours to complete/3 weeks at 3 hours a week
Assessment
Yes
Yes
Cost
Free
Free
Learning Outcomes
- Learn the fundamentals of artificial intelligence (AI) relevant to engineering design and optimization processes
- Enhance engineering design creativity and optimize strategies by applying AI techniques
- Discern appropriate applications of AI to ensure effective integration into workflows
- Interpret outputs generated by AI models accurately
- Learn new concepts from industry experts
- Gain a foundational understanding of AI tools
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Learning Content
- Introduction to Key Concepts and Fundamentals of Artificial Intelligence (AI) and Machine Learning
- Generative Design Techniques
- AI-Driven Optimization Algorithms
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Battery Systems Engineer/Data-driven Decision Making Complete A.I. & Machine Learning, Data Science Bootcamp
This course is focused on efficiency: never spend time on confusing, out-of-date, incomplete Machine Learning tutorials anymore. This comprehensive and project-based course will introduce you to all of the modern skills of a Data Scientist, with real-world projects to add to your portfolio.
Provider
Udemy
Udemy
Target
- Aspiring data scientists and machine learning engineers
- Beginners with no prior programming experience
- Individuals with some programming knowledge wanting to deepen their understanding of data science
- Professionals looking to transition into data science or machine learning roles
Sector
- Data Science
- Machine Learning
- Artificial Intelligence (AI)
- Technology/Information Technology (IT)
Area
- Data exploration and visualization
- Neural networks and deep learning
- Model evaluation and analysis
- Programming in Python
- Machine learning libraries (TensorFlow, Scikit-Learn, etc.)
- Data science projects and workflows
- Supervised and unsupervised learning techniques
- Data preparation and cleaning
- Advanced topics like transfer learning and ensemble learning
- Real-world applications and project development
Method
Online
Online
Certification
Yes
Yes
Duration
43.5 hours on-demand video
43.5 hours on-demand video
Assessment
No
No
Cost
€99.99
€99.99
Learning Outcomes
- Become a Data Scientist and get hired
- Master Machine Learning and use it on the job
- Deep Learning, Transfer Learning, and Neural Networks using TensorFlow 2.0
- Use modern tools that big tech companies like Google, Apple, Amazon, and Meta use
- Present Data Science projects to management and stakeholders
- Learn which Machine Learning model to choose for each type of problem
- Real-life case studies and projects to understand real-world applications
- Learn best practices in Data Science Workflow
- Implement Machine Learning algorithms
- Learn Python programming using the latest Python 3
- Improve Machine Learning Models
- Pre-process, clean, and analyze large datasets
- Build a portfolio of work to enhance your resume
- Set up a Developer Environment for Data Science and Machine Learning
- Understand Supervised and Unsupervised Learning
- Apply Machine Learning to Time Series data
Learning Content
- Introduction
- Machine Learning and Data Science Framework
- Data Science Environment Setup
- Pandas: Data Analysis
- Matplotlib: Plotting and Data Visualization
- Scikit-learn: Creating Machine Learning Models
- Supervised Learning: Classification + Regression
- Milestone Project 1: Supervised Learning (Classification)
- Milestone Project 2: Supervised Learning (Time Series Data)
- Data Engineering
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AI-Driven Recycling Specialist Workshops on HPCC Modeling, AI and Robotics for Manufacturing Sites
Workshops on HPCC modeling, AI and Robotics for manufacturing sites. We inspire SMEs to consider the usage of specific HPC-based tools that may boost their businesses.
Provider
i4ms
i4ms
Target
- Directors
- Plant Managers
- Engineers
- Operators
Sector
- Automotive
- Agriculture
- Chemical
- Computer - Software
- Construction
- Research & Development
Area
- Factory logistics automation
- High-Performance Computing (HPC) tools
- Internet of Things (IoT) applications in manufacturing
- OPIL system implementation and usage
Method
Online
Online
Certification
No
No
Duration
1h to 4h
1h to 4h
Assessment
No
No
Cost
Free
Free
Learning Outcomes
- Learn how to use IoT to automate factory logistics.
- Learn the functionality of OPIL (an IoT system for intra-factory logistics).
Learning Content
- Introduction to Automated Factory Logistics
- Architecture of OPIL
- OPIL IoT modules
- OPIL IoT nodes
- OPIL Deployment
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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
Learn More
AI-Driven Recycling Specialist Workshops on HPCC Modeling, AI and Robotics for Manufacturing Sites
Workshops on HPCC modeling, AI and Robotics for manufacturing sites. We inspire SMEs to consider the usage of specific HPC-based tools that may boost their businesses.
Provider
i4ms
i4ms
Target
- Directors
- Plant Managers
- Engineers
- Operators
Sector
- Automotive
- Agriculture
- Chemical
- Computer - Software
- Construction
- Research & Development
Area
- Factory logistics automation
- High-Performance Computing (HPC) tools
- Internet of Things (IoT) applications in manufacturing
- OPIL system implementation and usage
Method
Online
Online
Certification
No
No
Duration
1h to 4h
1h to 4h
Assessment
No
No
Cost
Free
Free
Learning Outcomes
- Learn how to use IoT to automate factory logistics.
- Learn the functionality of OPIL (an IoT system for intra-factory logistics).
Learning Content
- Introduction to Automated Factory Logistics
- Architecture of OPIL
- OPIL IoT modules
- OPIL IoT nodes
- OPIL Deployment
Learn More
AI-Driven Recycling Specialist
Using Artificial Intelligence in Strategic Analysis and Decision-Making Processes
Decision making in projects is a complex and dynamic process that depends on various project parameters and the project environment. Since each project is unique and has specific factors, a thorough analysis of the project context is necessary to develop an objective and reliable decision-making model. Complex projects often hold strategic importance for stakeholders in terms of their business strategy, brand image, and financial and economic opportunities. While front-end planning is essential, not all events and scenarios can be foreseen, especially for long-term projects involving multiple stakeholders. Therefore, strategic analysis is critical in the early phases of the project to identify and analyze major opportunity and risk factors for making informed strategic decisions. However, in the early phases the amount of information available is limited, and predictive tools may be necessary to forecast the project's future.
Provider
Pmi.org
Target
- Professionals involved in project management, decision-making, and strategic analysis
- Fresh Learners in integrating AI into their processes
Sector
- Project management
- Strategic decision-making
- Artificial intelligence applications in project contexts
Area
- Technology
- Consulting
- Construction
- Finance
Method
Online
Certification
Yes
Duration
9:00 a.m – 5:00 p.m
Assessment
No
Cost
- Member Price: US$881.00 (US$837.00 before 21 July)
- Nonmember Price: US$1326.00 (US$1260.00 before 21 July)
- Government Price: US$797.00
- Student Price: US$383.00
Learning Outcomes
- Understand the basics of artificial intelligence and its applications in strategic analysis and decision making.
- Evaluate the potential benefits and risks associated with using artificial intelligence in strategic analysis and decision making.
- Develop strategies and plans for integrating artificial intelligence into existing decision-making processes.
Learning Content
- Introduction to Artificial Intelligence (AI)
- Definition and history of AI
- Applications of AI in various industries
- AI tools and techniques for strategic analysis and decision-making
- Benefits and Risks of AI in Strategic Analysis and Decision-Making
- Potential benefits of using AI for decision-making
- Risks and limitations of AI in decision-making
- Ethical and legal considerations
- Types of AI Tools and Techniques
- Predictive analytics and modeling
- Neural networks and deep learning
- Visualization tools and techniques
- Real-World Examples of AI in Strategic Analysis and Decision-Making
- Case studies from various industries, such as finance, healthcare, and marketing
- Success stories and best practices for implementing AI in decision-making
- Integrating AI Into Existing Decision-Making Processes
- Strategies for incorporating AI into current processes
- Planning for the implementation of AI
- Change management and training for stakeholders
- Evaluating and Communicating Results
- Analyzing and interpreting the results of AI-based analysis and decision-making
- Communicating findings and recommendations to stakeholders
- Evaluating the effectiveness of AI-based decision-making
- Future Trends and Directions in AI for Strategic Analysis and Decision-Making
- Emerging AI tools and techniques
- Potential future applications of AI in decision-making
Learn More
Provider
Pmi.org
Pmi.org
Target
- Professionals involved in project management, decision-making, and strategic analysis
- Fresh Learners in integrating AI into their processes
Sector
- Project management
- Strategic decision-making
- Artificial intelligence applications in project contexts
Area
- Technology
- Consulting
- Construction
- Finance
Method
Online
Online
Certification
Yes
Yes
Duration
9:00 a.m – 5:00 p.m
9:00 a.m – 5:00 p.m
Assessment
No
No
Cost
- Member Price: US$881.00 (US$837.00 before 21 July)
- Nonmember Price: US$1326.00 (US$1260.00 before 21 July)
- Government Price: US$797.00
- Student Price: US$383.00
Learning Outcomes
- Understand the basics of artificial intelligence and its applications in strategic analysis and decision making.
- Evaluate the potential benefits and risks associated with using artificial intelligence in strategic analysis and decision making.
- Develop strategies and plans for integrating artificial intelligence into existing decision-making processes.
Learning Content
- Introduction to Artificial Intelligence (AI)
- Definition and history of AI
- Applications of AI in various industries
- AI tools and techniques for strategic analysis and decision-making
- Benefits and Risks of AI in Strategic Analysis and Decision-Making
- Potential benefits of using AI for decision-making
- Risks and limitations of AI in decision-making
- Ethical and legal considerations
- Types of AI Tools and Techniques
- Predictive analytics and modeling
- Neural networks and deep learning
- Visualization tools and techniques
- Real-World Examples of AI in Strategic Analysis and Decision-Making
- Case studies from various industries, such as finance, healthcare, and marketing
- Success stories and best practices for implementing AI in decision-making
- Integrating AI Into Existing Decision-Making Processes
- Strategies for incorporating AI into current processes
- Planning for the implementation of AI
- Change management and training for stakeholders
- Evaluating and Communicating Results
- Analyzing and interpreting the results of AI-based analysis and decision-making
- Communicating findings and recommendations to stakeholders
- Evaluating the effectiveness of AI-based decision-making
- Future Trends and Directions in AI for Strategic Analysis and Decision-Making
- Emerging AI tools and techniques
- Potential future applications of AI in decision-making
AI-Driven Recycling Specialist Data Analytics - Mining and Analysis of Big Data
In this online data analysis course "Data Analytics - Mining and Analysis of Big Data," you will be introduced to the concept of big data and a number of techniques that are used to analyze and interpret it. The course starts by introducing you to big data and listing the four V’s of big data. You will learn about associative rule mining, when association can be applied, and the patterns that arise in mining. In the second module, you will explore clustering analysis, examining the difference between clustering and classification, and the various types of clustering, including K-means clustering and K-medoids. The final module focuses on online and active learning, experimentation, and the contexts of creating data online and offline. You will also delve into the n-arm bandit problem and discover solutions for the multi-arm bandit problem. This free online course is perfect for professionals in data science and analysis and learners eager to master big data mining and clustering techniques.
Provider
alison.com
alison.com
Target
- Data science and data analysis professionals
- Learners interested in big data analysis
Sector
- Data analytics
- Mining and analysis of big data
Area
- Technology
- Data science and analytics
Method
Online
Online
Certification
CPD Accreditation
CPD Accreditation
Duration
1.5-3 Hours
1.5-3 Hours
Assessment
Final Assessment
Final Assessment
Cost
Free
Free
Learning Outcomes
- Define association rule mining
- Explain mining frequent patterns and rules
- Distinguish between clustering and classification
- Define the apriori algorithm
- List the four V’s of big data
- Explain why social media data can be hard to disambiguate
- Define K-means clustering
- Explain K-medoids
- Describe some solutions for the multi-arm bandit problem
Learning Content
- Module 1: Introduction to Associative Rule Mining
- Learn about frequent patterns and rules in mining
- Understand when association rules can be applied
- Module 2: Introduction to Big Data
- Learn about the four V’s of big data
- Understand why social media data can be hard to disambiguate
- Module 3: Introduction to Clustering Analysis
- Understand why clustering is used
- Learn about different types of clustering
- Module 4: Experimentation and Active Learning
- Learn about online and offline contexts for creating data
- Understand the n-arm bandit problem
- Module 5: Course Assessment
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AI-Driven Recycling Specialist / Digital Product Passport Integration Foundations of Li-ion Batteries, Battery Management Systems
There are no pre-requisites to this course and no previous knowledge of Battery Technology or Battery Management Systems is needed.
- Are you looking for a successful career in the Electric Vehicle / Battery Technology industry?
- Are you a startup creating products and services for the EV / BMS industry?
- Are you an established business in the EV industry looking to up-skill your staff?
- Are you a university or college looking to train students for the EV industry?
- Are you a student aspiring for a fulfilling career in the EV industry?
Provider
Udemy
Udemy
Target
- Individuals seeking a career in the Electric Vehicle (EV) / Battery Technology industry
- Startups developing products and services for the EV / BMS industry
- Established businesses in the EV sector aiming to up-skill their employees
- Universities and colleges training students for careers in the EV industry
- Students aspiring to enter the EV industry
Sector
- Electric Vehicle Industry
- Battery Technology Sector
Area
- Battery Management Systems (BMS)
- Hardware and Software Integration in BMS
- Failure Analysis and Debugging in BMS
Method
Online
Online
Certification
Yes
Yes
Duration
5 hours on-demand video
5 hours on-demand video
Assessment
No
No
Cost
€39.99
€39.99
Learning Outcomes
- Foundations of lithium-ion batteries
- Foundations of battery management
- Foundations of electric vehicle batteries
- Practical understanding of batteries and battery management systems
Learning Content
- Introduction
- BMS basics
- Lithium-ion cell basics
- Experiments with Lithium-ion
- Power conditioning and filtering
- BMS design - signal acquisition
- Powering the BMS circuitry
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AI-Driven Recycling Specialist Machine Learning: Modern Computer Vision & Generative AI
Course Highlights:
- KerasCV Library: We start by harnessing the power of the KerasCV library, which seamlessly integrates with popular deep learning backends like Tensorflow, PyTorch, and JAX. KerasCV simplifies the process of writing deep learning code, making it accessible and user-friendly.
- Image Classification: Gain proficiency in image classification techniques. Learn how to leverage pre-trained models with just one line of code, and discover the art of fine-tuning these models to suit your specific datasets and applications.
- Object Detection: Dive into the fascinating world of object detection. Master the art of using pre-trained models for object detection tasks with minimal effort. Moreover, explore the process of fine-tuning these models and learn how to create custom object detection datasets using the LabelImg GUI program.
Provider
Udemy
Udemy
Target
- Aspiring machine learning practitioners
- Data scientists interested in image analysis and generative modeling
- Software developers looking to expand their skills in AI
- Students and professionals in computer science or related fields
Sector
- Information Technology
- Artificial Intelligence
- Data Science
- Creative Industries
Area
- Computer Vision
- Generative AI
- Deep Learning with KerasCV
- Image Classification and Object Detection
Method
Online
Online
Certification
Yes
Yes
Duration
6.5 hours on-demand video
6.5 hours on-demand video
Assessment
No
No
Cost
€69.99
€69.99
Learning Outcomes
- Computer vision
- How to do image classification / image recognition with a pretrained model and fine-tuning / transfer learning
- How to do object detection with a pretrained model and fine-tuning / transfer learning
Learning Content
- Introduction
- Image Classification, Fine-Tuning and Transfer Learning
- Object Detection
- Generative AI with Stable Diffusion
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Using Artificial Intelligence in Strategic Analysis and Decision-Making Processes
Mid Level Employee
Foundations
Data Analysis
Strong skills in data gathering, preprocessing, and analysis to handle data from online monitoring and deep testing of LIB cells.
Digital Product Passport Integration
Expertise in implementing and managing Digital Product Passports to enhance traceability, compliance, and efficiency in the battery lifecycle.
Computer Vision
Experience with computer vision techniques to enhance collaborative robots' efficiency in disassembly processes.
Robotics Integration
Knowledge of integrating AI with robotic systems for tasks like precision drilling and disassembly.
Data Analytics - Mining and Analysis of Big Data
Junior (Fresh Employee)
Foundations
Foundations of Li-ion batteries, battery management systems
Junior (Fresh Employee)
Awareness
DPP Training
Mid Level Employee
Foundations
Mastering in Advanced Deep Learning Computer Vision™
Mid Level Employee
Extended Know-How
Asset Administration Shell
Proficiency in using asset administration shells to manage and integrate digital twins of battery components, enabling seamless data exchange and enhanced lifecycle management.
Machine Learning: Modern Computer Vision & Generative AI
Mid Level Employee
Foundations
Workshops on HPCC modeling, AI and Robotics for Manufacturing sites
Mid Level Employee
Foundations
Digital Twins
Junior (Fresh Employee)
Foundations
Algorithm Development
Ability to develop AI-driven optimization algorithms for mechanical recycling processes.
AI for Design and Optimization
Mid Level Employee
Foundations