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AI+ Video Practitioner™
Embrace the future of AI in video to inspire innovation and craft immersive visual experiences
* Beginner-Friendly Pathway: A perfect starting point for learners exploring AI-driven video creation, editing, and automation
* End-to-End Mastery: Covers AI video fundamentals, advanced tools, generative video workflows, and responsible content creation
* Industry-Aligned Skills: Understand how AI video technologies shape marketing, education, entertainment, and business communication
* Practical Execution: Provides guided exercises, templates, and workflows to help you produce professional-quality AI-powered videos confidently
Module 1: Foundation of AI in Video Integration
* 1.1 Basics of Video Processing
* 1.2 Introduction to AI in Video
* 1.3 Toolkits and Framework
* 1.4 Use Case: AI-enhanced Video Compression for Streaming Platforms
* 1.5 Case Study: YouTube’s AI-Driven Transcoding System Module 2: Preparing Video Data for AI
* 2.1 Data Preparation for AI Models
* 2.2 Preprocessing and Augmenting Frames
* 2.3 Storage and Workflow Management
* 2.4 Use Case: Building AI-ready Video Datasets for Autonomous Driving Applications
* 2.5 Case Study: Tesla’s In-house Pipeline for Labeling Driving Scenarios across Multiple Geographies using Video Footage
* 2.6 Hands-On: Video Annotation using CVAT Tool, and Organizing them for Model Training Module 3: Machine Learning for Video Analysis
* 3.1 Video Classification and Tagging
* 3.2 Object Detection and Movement Tracking
* 3.3 Action and Behavior Recognition
* 3.4 Use Case: Smart Surveillance Systems Detecting Abandoned Objects in Real Time
* 3.5 Case Study: Dubai Smart City’s AI Implementation for Object Recognition
* 3.6 Hands-On: Train YOLO on Sample Security Footage to Detect and Track Objects Module 4: Generative AI in Video
* 4.1 Generating Synthetic Video with GANs
* 4.2 AI-Driven Animation and Avatars
* 4.3 Ethical Use of Generative Content
* 4.4 Use Case: Auto-Generation of Product Explainer Videos using Avatars and Synthesized Narration
* 4.5 Case Study: Synthesia’s Solution Enabling Businesses to Create AI-Driven Training and Marketing Videos
* 4.6 Hands-On: Generate a Deepfake or AI Avatar using AKOOL, and Explore Face Alignment and Identity Swapping Module 5: Enhancing Video with AI
* 5.1 Super-Resolution and Restoration
* 5.2 Real-Time Video Enhancement
* 5.3 Making Video More Inclusive
* 5.4 Use Case: Streaming Platforms using AI to Enhance Resolution and Reduce Latency for Mobile Users.
* 5.5 Case Study: DeOldify’s Impact in Reviving Historical Video Archives by Upscaling and Colorizing Black-and-White Footage.
* 5.6 Hands-On: Use AI4Video to Enhance a Sample Low-Resolution Black-and-White Video and Visualize Improvement Module 6: Interactive and Immersive AI Video
* 6.1 AI in AR and Mixed Reality
* 6.2 Intelligent Video Editing
* 6.3 Viewer Engagement & Adaptation
* 6.4 Use Case: Live Sports Broadcasters using AR to Overlay Player Stats during Gameplay
* 6.5 Case Study: NFL and AWS Collaboration to Deliver Real-Time Performance Insights via Augmented Visuals.
* 6.6 Hands-On: Creating a Highlight Video from a Video Clip using Clipchamp Module 7: AI in Video Surveillance and Compliance
* 7.1 Security and Monitoring Systems
* 7.2 Automated Content Moderation
* 7.3 Addressing Privacy and Ethics
* 7.4 Use Case: Automated Real-Time Access Control in Corporate Offices Using Facial Authentication.
* 7.5 Case Study: Amazon Go’s Cashier-less Stores Using Computer Vision for Security and Consumer Behavior Tracking
* 7.6 Hands-On: Implement Facial Detection and Access Control Simulation using OpenCV and a Basic Recognition Model Module 8: Future of AI+ Video Practitioner™
* 8.1 Trends and Emerging Technologies
* 8.2 AI Applications by Industry
* 8.3 Careers and Professional Growth Tools you will explore
* TensorFlow
* PyTorch
* OpenCV
* MediaPipe
* Runway ML
* Synthesia Studio
* DeepFaceLab
* Adobe Sensei
* DaVinci Resolve Neural Engine
* Runway
* Pika Labs
* Kaiber AI
* DeepBrain AI Studio
* NVIDIA Maxine SDK
* Google Video AI API
* FFmpeg Automation Tools
* Unreal Engine with AI Plugins
* Blender AI Add-ons
* Stability Video Diffusion
* Generative Video Editing Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€895
Klassikaal
max 12
1 dag
AI+ Video Practitioner™ eLearning
Embrace the future of AI in video to inspire innovation and craft immersive visual experiences
* Beginner-Friendly Pathway: A perfect starting point for learners exploring AI-driven video creation, editing, and automation
* End-to-End Mastery: Covers AI video fundamentals, advanced tools, generative video workflows, and responsible content creation
* Industry-Aligned Skills: Understand how AI video technologies shape marketing, education, entertainment, and business communication
* Practical Execution: Provides guided exercises, templates, and workflows to help you produce professional-quality AI-powered videos confidently
Module 1: Foundation of AI in Video Integration
* 1.1 Basics of Video Processing
* 1.2 Introduction to AI in Video
* 1.3 Toolkits and Framework
* 1.4 Use Case: AI-enhanced Video Compression for Streaming Platforms
* 1.5 Case Study: YouTube’s AI-Driven Transcoding System Module 2: Preparing Video Data for AI
* 2.1 Data Preparation for AI Models
* 2.2 Preprocessing and Augmenting Frames
* 2.3 Storage and Workflow Management
* 2.4 Use Case: Building AI-ready Video Datasets for Autonomous Driving Applications
* 2.5 Case Study: Tesla’s In-house Pipeline for Labeling Driving Scenarios across Multiple Geographies using Video Footage
* 2.6 Hands-On: Video Annotation using CVAT Tool, and Organizing them for Model Training Module 3: Machine Learning for Video Analysis
* 3.1 Video Classification and Tagging
* 3.2 Object Detection and Movement Tracking
* 3.3 Action and Behavior Recognition
* 3.4 Use Case: Smart Surveillance Systems Detecting Abandoned Objects in Real Time
* 3.5 Case Study: Dubai Smart City’s AI Implementation for Object Recognition
* 3.6 Hands-On: Train YOLO on Sample Security Footage to Detect and Track Objects Module 4: Generative AI in Video
* 4.1 Generating Synthetic Video with GANs
* 4.2 AI-Driven Animation and Avatars
* 4.3 Ethical Use of Generative Content
* 4.4 Use Case: Auto-Generation of Product Explainer Videos using Avatars and Synthesized Narration
* 4.5 Case Study: Synthesia’s Solution Enabling Businesses to Create AI-Driven Training and Marketing Videos
* 4.6 Hands-On: Generate a Deepfake or AI Avatar using AKOOL, and Explore Face Alignment and Identity Swapping Module 5: Enhancing Video with AI
* 5.1 Super-Resolution and Restoration
* 5.2 Real-Time Video Enhancement
* 5.3 Making Video More Inclusive
* 5.4 Use Case: Streaming Platforms using AI to Enhance Resolution and Reduce Latency for Mobile Users.
* 5.5 Case Study: DeOldify’s Impact in Reviving Historical Video Archives by Upscaling and Colorizing Black-and-White Footage.
* 5.6 Hands-On: Use AI4Video to Enhance a Sample Low-Resolution Black-and-White Video and Visualize Improvement Module 6: Interactive and Immersive AI Video
* 6.1 AI in AR and Mixed Reality
* 6.2 Intelligent Video Editing
* 6.3 Viewer Engagement & Adaptation
* 6.4 Use Case: Live Sports Broadcasters using AR to Overlay Player Stats during Gameplay
* 6.5 Case Study: NFL and AWS Collaboration to Deliver Real-Time Performance Insights via Augmented Visuals.
* 6.6 Hands-On: Creating a Highlight Video from a Video Clip using Clipchamp Module 7: AI in Video Surveillance and Compliance
* 7.1 Security and Monitoring Systems
* 7.2 Automated Content Moderation
* 7.3 Addressing Privacy and Ethics
* 7.4 Use Case: Automated Real-Time Access Control in Corporate Offices Using Facial Authentication.
* 7.5 Case Study: Amazon Go’s Cashier-less Stores Using Computer Vision for Security and Consumer Behavior Tracking
* 7.6 Hands-On: Implement Facial Detection and Access Control Simulation using OpenCV and a Basic Recognition Model Module 8: Future of AI+ Video Practitioner™
* 8.1 Trends and Emerging Technologies
* 8.2 AI Applications by Industry
* 8.3 Careers and Professional Growth Tools you will explore
* TensorFlow
* PyTorch
* OpenCV
* MediaPipe
* Runway ML
* Synthesia Studio
* DeepFaceLab
* Adobe Sensei
* DaVinci Resolve Neural Engine
* Runway
* Pika Labs
* Kaiber AI
* DeepBrain AI Studio
* NVIDIA Maxine SDK
* Google Video AI API
* FFmpeg Automation Tools
* Unreal Engine with AI Plugins
* Blender AI Add-ons
* Stability Video Diffusion
* Generative Video Editing Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€200
E-Learning
max 999
1 dag
AI+ Supply Chain Practitioner™
Formerly known as AI+ Supply Chain™
Transforming Supply Chain Management
* Comprehensive Learning: Covers logistics, operations, and supply chain digitization
* Advanced Supply Strategies: Develop innovative supply strategies and workflows
* Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
* Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency
Module 1: Introduction to Artificial Intelligence in Supply Chain
* 1.1 Overview of Artificial Intelligence in Supply Chain Management (SCM)
* 1.2 Transforming Supply Chains with AI
* 1.3 Ethical Implications of AI in Supply Chains Module 2: Advanced AI Techniques for Supply Chain
* 2.1 Machine Learning in Supply Chain
* 2.2 Expert Systems in SCM
* 2.3 Integrating Images and Text in Supply Chain AI Module 3: Generative AI in Supply Chain Management
* 3.1 The Origin of Generative AI
* 3.2 Generative AI in Revenue Management and Demand Forecasting
* 3.3 Transformer and LSTM Architectures in Generative AI Module 4: Supply Chain Digitization
* 4.1 Introduction to Supply Chain Digitization
* 4.2 Supply Chain Integration and Push-Pull Strategies
* 4.3 Supply Chain Resiliency, Planning and Sustainability Module 5: Intelligent Driven Supply Chain Management
* 5.1 Introduction to Smart SCM
* 5.2 Employing Smart SCM and Prompt Engineering
* 5.3 Future Trends of Smart SCM Module 6: Industry Aspects of Advanced SCM
* 6.1 Introduction to Industrial SCM
* 6.2 Business Value from AI and Gen AI in Supply Chain
* 6.3 Risks and Challenges of Adopting AI and Gen AI in Industrial SCM Module 7: Policies of Logistics Management in Supply Chain with AI
* 7.1 Role of Supply Chain Management in the Organization
* 7.2 Warehousing Strategy for Efficient Supply Chain Management
* 7.3 Technical Coverage of SCM with Multi-Dimensional Aspects Module 8: Supply Chain Masterclass with AI Assistance
* 8.1 Supplier Selection and Relationship Management with AI
* 8.2 Mastering Advancements in SCM with Modern Artefacts Optional Module: AI Agents for Supply Chain
* What Are AI Agents
* What Are AI Agents in Logistics and Supply Chain
* Applications & Trends of AI Agents in Supply Chain
* How Does an AI Agent Work
* Core Characteristics of AI Agents
* Key Advantages of AI Agents in Logistics and Supply Chain
* Types of AI Agents Tools you will explore
* LeewayHertz (ZBrain)
* C3.ai
* Coupa (LLamasoft)
* Zebra (Workcloud Demand Intelligence Suite)
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€895
Klassikaal
max 12
1 dag
AI+ Supply Chain Practitioner™ eLearning
Formerly known as AI+ Supply Chain™
Transforming Supply Chain Management
* Comprehensive Learning: Covers logistics, operations, and supply chain digitization
* Advanced Supply Strategies: Develop innovative supply strategies and workflows
* Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
* Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency
Module 1: Introduction to Artificial Intelligence in Supply Chain
* 1.1 Overview of Artificial Intelligence in Supply Chain Management (SCM)
* 1.2 Transforming Supply Chains with AI
* 1.3 Ethical Implications of AI in Supply Chains Module 2: Advanced AI Techniques for Supply Chain
* 2.1 Machine Learning in Supply Chain
* 2.2 Expert Systems in SCM
* 2.3 Integrating Images and Text in Supply Chain AI Module 3: Generative AI in Supply Chain Management
* 3.1 The Origin of Generative AI
* 3.2 Generative AI in Revenue Management and Demand Forecasting
* 3.3 Transformer and LSTM Architectures in Generative AI Module 4: Supply Chain Digitization
* 4.1 Introduction to Supply Chain Digitization
* 4.2 Supply Chain Integration and Push-Pull Strategies
* 4.3 Supply Chain Resiliency, Planning and Sustainability Module 5: Intelligent Driven Supply Chain Management
* 5.1 Introduction to Smart SCM
* 5.2 Employing Smart SCM and Prompt Engineering
* 5.3 Future Trends of Smart SCM Module 6: Industry Aspects of Advanced SCM
* 6.1 Introduction to Industrial SCM
* 6.2 Business Value from AI and Gen AI in Supply Chain
* 6.3 Risks and Challenges of Adopting AI and Gen AI in Industrial SCM Module 7: Policies of Logistics Management in Supply Chain with AI
* 7.1 Role of Supply Chain Management in the Organization
* 7.2 Warehousing Strategy for Efficient Supply Chain Management
* 7.3 Technical Coverage of SCM with Multi-Dimensional Aspects Module 8: Supply Chain Masterclass with AI Assistance
* 8.1 Supplier Selection and Relationship Management with AI
* 8.2 Mastering Advancements in SCM with Modern Artefacts Optional Module: AI Agents for Supply Chain
* What Are AI Agents
* What Are AI Agents in Logistics and Supply Chain
* Applications & Trends of AI Agents in Supply Chain
* How Does an AI Agent Work
* Core Characteristics of AI Agents
* Key Advantages of AI Agents in Logistics and Supply Chain
* Types of AI Agents Tools you will explore
* LeewayHertz (ZBrain)
* C3.ai
* Coupa (LLamasoft)
* Zebra (Workcloud Demand Intelligence Suite)
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€200
E-Learning
max 999
1 dag
AI+ Chief AI Officer Practitioner™
AI Leadership for Chief Officers: Driving Innovation and Intelligence
* Leadership Upgrade: Equip C-suite executives to lead AI-driven innovation
* Efficiency Focus: Use AI tools to optimize operations, decision-making, and resources
* Strategic Role: Aligns AI implementation with business intelligence goals
* Course + Exam: Combines theory and practical insights in a compact format
Module 1: Foundations of AI and Leadership in the Digital Era
* 1.1 Defining Artificial Intelligence
* 1.2 Key AI Technologies
* 1.3 The CAIO’s Unique Role
* 1.4 Navigating Cybersecurity Challenges
* 1.5 Establishing Cross-Departmental Collaboration
* 1.6 Case Study Module 2: Crafting a Strategic AI Roadmap
* 2.1 Aligning AI with Business Objectives
* 2.2 Setting Measurable Goals
* 2.3 Identifying Opportunities for Innovation
* 2.4 Engaging Stakeholders Across Departments
* 2.5 Monitoring Progress and Adjusting Plans
* 2.6 Case Study Module 3: Building a High-Performance AI Team
* 3.1 Key Roles in an AI Team
* 3.2 Recruitment Strategies for Top Talent
* 3.3 Cultivating a Collaborative Culture
* 3.4 Continuous Learning Initiatives
* 3.5 Evaluating Team Performance
* 3.6 Case Study Module 4: Ethics in AI Governance and Risk Management
* 4.1 Integrating Ethical Frameworks into AI Development
* 4.2 Conducting Ethical Impact Assessments
* 4.3 Developing Risk Mitigation Strategies
* 4.4 Establishing Transparency Protocols
* 4.5 AI Governance Models and Frameworks
* 4.6 Case Study Module 5: Data-Driven Decision-Making and Business Impact Assessment
* 5.1 The Role of Data in AI Initiatives
* 5.2 Business Impact Assessment Frameworks
* 5.3 Measuring ROI from AI Investments
* 5.4 Hypothesis Testing in AI Projects
* 5.5 Resource Allocation Strategies
* 5.6 Case Study Module 6: Driving Organization: Wide Adoption of AI
* 6.1 Creating Change Management Strategies
* 6.2 Communicating the Value of AI Initiatives
* 6.3 Addressing Resistance to Change
* 6.4 Metrics for Success Evaluation
* 6.5 Case Study Module 7: Leveraging Generative AI for Business Innovation
* 7.1 Understanding Generative AI Capabilities
* 7.2 Identifying Areas for Innovation with Generative AI
* 7.3 Integrating Generative Solutions into Business Processes
* 7.4 Managing Risks Associated with Generative Applications
* 7.5 Creating Interdepartmental Synergies with Generative AI
* 7.6 Case Study Module 8: Capstone Project
* 8.1 Project Overview and Objectives
* 8.2 Collaborative Work Sessions
* 8.3 Presentation Skills Workshop
* 8.4 Final Presentations and Constructive Feedback
* 8.5 Reflection on Key Takeaways from the Course Experience Optional Module: AI Agents for Chief AI Officer
* 1. What Are AI Agents
* 2. Key Capabilities of AI Agents for the Chief AI Officer
* 3. Applications and Trends of AI Agents for the Chief AI Officer
* 4. How Does an AI Agent Work
* 5. Core Characteristics of AI Agents
* 6. Types of AI Agents Tools you will explore
* LeewayHertz (ZBrain)
* C3.ai
* Coupa (LLamasoft)
* Zebra (Workcloud Demand Intelligence Suite)
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€895
Klassikaal
max 12
1 dag
AI+ Chief AI Officer Practitioner™ eLearning
AI Leadership for Chief Officers: Driving Innovation and Intelligence
* Leadership Upgrade: Equip C-suite executives to lead AI-driven innovation
* Efficiency Focus: Use AI tools to optimize operations, decision-making, and resources
* Strategic Role: Aligns AI implementation with business intelligence goals
* Course + Exam: Combines theory and practical insights in a compact format
Module 1: Foundations of AI and Leadership in the Digital Era
* 1.1 Defining Artificial Intelligence
* 1.2 Key AI Technologies
* 1.3 The CAIO’s Unique Role
* 1.4 Navigating Cybersecurity Challenges
* 1.5 Establishing Cross-Departmental Collaboration
* 1.6 Case Study Module 2: Crafting a Strategic AI Roadmap
* 2.1 Aligning AI with Business Objectives
* 2.2 Setting Measurable Goals
* 2.3 Identifying Opportunities for Innovation
* 2.4 Engaging Stakeholders Across Departments
* 2.5 Monitoring Progress and Adjusting Plans
* 2.6 Case Study Module 3: Building a High-Performance AI Team
* 3.1 Key Roles in an AI Team
* 3.2 Recruitment Strategies for Top Talent
* 3.3 Cultivating a Collaborative Culture
* 3.4 Continuous Learning Initiatives
* 3.5 Evaluating Team Performance
* 3.6 Case Study Module 4: Ethics in AI Governance and Risk Management
* 4.1 Integrating Ethical Frameworks into AI Development
* 4.2 Conducting Ethical Impact Assessments
* 4.3 Developing Risk Mitigation Strategies
* 4.4 Establishing Transparency Protocols
* 4.5 AI Governance Models and Frameworks
* 4.6 Case Study Module 5: Data-Driven Decision-Making and Business Impact Assessment
* 5.1 The Role of Data in AI Initiatives
* 5.2 Business Impact Assessment Frameworks
* 5.3 Measuring ROI from AI Investments
* 5.4 Hypothesis Testing in AI Projects
* 5.5 Resource Allocation Strategies
* 5.6 Case Study Module 6: Driving Organization: Wide Adoption of AI
* 6.1 Creating Change Management Strategies
* 6.2 Communicating the Value of AI Initiatives
* 6.3 Addressing Resistance to Change
* 6.4 Metrics for Success Evaluation
* 6.5 Case Study Module 7: Leveraging Generative AI for Business Innovation
* 7.1 Understanding Generative AI Capabilities
* 7.2 Identifying Areas for Innovation with Generative AI
* 7.3 Integrating Generative Solutions into Business Processes
* 7.4 Managing Risks Associated with Generative Applications
* 7.5 Creating Interdepartmental Synergies with Generative AI
* 7.6 Case Study Module 8: Capstone Project
* 8.1 Project Overview and Objectives
* 8.2 Collaborative Work Sessions
* 8.3 Presentation Skills Workshop
* 8.4 Final Presentations and Constructive Feedback
* 8.5 Reflection on Key Takeaways from the Course Experience Optional Module: AI Agents for Chief AI Officer
* 1. What Are AI Agents
* 2. Key Capabilities of AI Agents for the Chief AI Officer
* 3. Applications and Trends of AI Agents for the Chief AI Officer
* 4. How Does an AI Agent Work
* 5. Core Characteristics of AI Agents
* 6. Types of AI Agents Tools you will explore
* LeewayHertz (ZBrain)
* C3.ai
* Coupa (LLamasoft)
* Zebra (Workcloud Demand Intelligence Suite)
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€200
E-Learning
max 999
1 dag
AI+ Sustainability Practitioner™
Formerly known as AI+ Sustainability™
Accelerate Sustainability with AI for smarter, greener progress
* Drive Sustainable Innovation: Harness the Power of Advanced AI
* AI for Greener Decisions: Explore carbon footprint analytics, resource optimization, and climate-impact modelling.
* Strategic Sustainability Impact: Learn to design data-driven, eco-focused frameworks that support long-term environmental goals.
* Future-Ready Tools: Includes lifecycle assessment tools, emission-tracking AI, and smart energy-management systems.
* Efficiency & Responsibility: Boost operational efficiency, reduce waste, and accelerate your organization’s journey towards a cleaner, climate-conscious future.
Module 1: Introduction to AI and Sustainability
* 1.1 Overview of Artificial Intelligence
* 1.2 Introduction to Sustainability
* 1.3 Sustainability Challenges
* 1.4 AI for Green
* 1.5 Case Study: AI Models for Climate Change Prediction
* 1.6 Hands On: Visualizing Global CO₂ Emissions Trends with GPT Module 2: AI Techniques for Sustainability Solutions
* 2.1 Introduction to Machine Learning for Sustainability
* 2.2 Supervised Learning for Environmental Impact
* 2.3 Unsupervised Learning for Environmental Insights
* 2.4 Reinforcement Learning for Sustainable Systems
* 2.5 Green AI: Sustainable AI Models
* 2.6 Hands-On Module 3: AI for Climate Change Mitigation
* 3.1 AI in Climate Modeling
* 3.2 AI for Renewable Energy Integration
* 3.3 Carbon Footprint Reduction
* 3.4 Case Study: Optimizing Wind Turbine Operations with AI
* 3.5 Hands-On Exercises Module 4: AI in Sustainable Energy Systems
* 4.1 AI for Energy Optimization
* 4.2 Renewable Energy Integration
* 4.3 AI in Energy Storage and Efficiency
* 4.4 Case Study: AI-Powered Smart Grids: Optimizing Energy Distribution and Integrating Renewables
* 4.5 Hands-On Exercises: Optimizing Smart Grid Load Balancing Module 5: AI for Sustainable Agriculture
* 5.1 Precision Agriculture and Resource Optimization
* 5.2 AI for Pest and Disease Detection
* 5.3 Sustainable Farming and Decision Support Systems
* 5.4 Case Study: AI in Precision Agriculture
* 5.5 Hands-On: Predicting Crop Yields with Machine Learning Module 6: AI in Waste Management and Circular Economy
* 6.1 AI for Waste Sorting and Recycling
* 6.2 AI for Waste-to-Energy Solutions
* 6.3 Circular Economy and Resource Recovery
* 6.4 Case Study: AI for Waste Sorting and Recycling
* 6.5 Hands-On: Building a Waste Sorting Classifier with AI Module 7: AI for Biodiversity Conservation and Environmental Monitoring
* 7.1 AI in Remote Sensing for Environmental Monitoring
* 7.2 Wildlife Tracking and Conservation
* 7.3 AI for Ecosystem Health Monitoring
* 7.4 Case Study: AI for Deforestation Monitoring
* 7.5 Hands-On: Detecting Deforestation Using Satellite Imagery Module 8: AI for Water Resource Management
* 8.1 AI for Water Consumption Prediction
* 8.2 AI for Smart Irrigation Systems
* 8.3 Water Quality Monitoring and Analysis
* 8.4 Case Study: AI for Smart Irrigation Systems
* 8.5 Hands-On: Optimizing Irrigation Systems with AI Module 9: AI for Sustainable Cities and Smart Urban Development
* 9.1 AI in Smart City Infrastructure
* 9.2 Sustainable Mobility and Transportation
* 9.3 AI in Urban Resource Optimization
* 9.4 Case Study: AI for Urban Air Quality Monitoring
* 9.5 Hands-On: Optimizing Traffic Flow and Reducing Emissions with AI-Driven Smart Traffic Management Tools you will explore
* TensorFlow
* PyTorch
* Python
* Climate Prediction
* AI-Driven Energy Management Systems
* AI-Based Resource Optimization Tools
* Machine Learning for Waste Reduction
* Smart Grid Optimization Software
* Environmental Data Visualization Platforms
* Sustainability Analytics Frameworks
* AI for Biodiversity Conservation
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€895
Klassikaal
max 12
1 dag
AI+ Sustainability Practitioner™ eLearning
Formerly known as AI+ Sustainability™
Accelerate Sustainability with AI for smarter, greener progress
* Drive Sustainable Innovation: Harness the Power of Advanced AI
* AI for Greener Decisions: Explore carbon footprint analytics, resource optimization, and climate-impact modelling.
* Strategic Sustainability Impact: Learn to design data-driven, eco-focused frameworks that support long-term environmental goals.
* Future-Ready Tools: Includes lifecycle assessment tools, emission-tracking AI, and smart energy-management systems.
* Efficiency & Responsibility: Boost operational efficiency, reduce waste, and accelerate your organization’s journey towards a cleaner, climate-conscious future.
Module 1: Introduction to AI and Sustainability
* 1.1 Overview of Artificial Intelligence
* 1.2 Introduction to Sustainability
* 1.3 Sustainability Challenges
* 1.4 AI for Green
* 1.5 Case Study: AI Models for Climate Change Prediction
* 1.6 Hands On: Visualizing Global CO₂ Emissions Trends with GPT Module 2: AI Techniques for Sustainability Solutions
* 2.1 Introduction to Machine Learning for Sustainability
* 2.2 Supervised Learning for Environmental Impact
* 2.3 Unsupervised Learning for Environmental Insights
* 2.4 Reinforcement Learning for Sustainable Systems
* 2.5 Green AI: Sustainable AI Models
* 2.6 Hands-On Module 3: AI for Climate Change Mitigation
* 3.1 AI in Climate Modeling
* 3.2 AI for Renewable Energy Integration
* 3.3 Carbon Footprint Reduction
* 3.4 Case Study: Optimizing Wind Turbine Operations with AI
* 3.5 Hands-On Exercises Module 4: AI in Sustainable Energy Systems
* 4.1 AI for Energy Optimization
* 4.2 Renewable Energy Integration
* 4.3 AI in Energy Storage and Efficiency
* 4.4 Case Study: AI-Powered Smart Grids: Optimizing Energy Distribution and Integrating Renewables
* 4.5 Hands-On Exercises: Optimizing Smart Grid Load Balancing Module 5: AI for Sustainable Agriculture
* 5.1 Precision Agriculture and Resource Optimization
* 5.2 AI for Pest and Disease Detection
* 5.3 Sustainable Farming and Decision Support Systems
* 5.4 Case Study: AI in Precision Agriculture
* 5.5 Hands-On: Predicting Crop Yields with Machine Learning Module 6: AI in Waste Management and Circular Economy
* 6.1 AI for Waste Sorting and Recycling
* 6.2 AI for Waste-to-Energy Solutions
* 6.3 Circular Economy and Resource Recovery
* 6.4 Case Study: AI for Waste Sorting and Recycling
* 6.5 Hands-On: Building a Waste Sorting Classifier with AI Module 7: AI for Biodiversity Conservation and Environmental Monitoring
* 7.1 AI in Remote Sensing for Environmental Monitoring
* 7.2 Wildlife Tracking and Conservation
* 7.3 AI for Ecosystem Health Monitoring
* 7.4 Case Study: AI for Deforestation Monitoring
* 7.5 Hands-On: Detecting Deforestation Using Satellite Imagery Module 8: AI for Water Resource Management
* 8.1 AI for Water Consumption Prediction
* 8.2 AI for Smart Irrigation Systems
* 8.3 Water Quality Monitoring and Analysis
* 8.4 Case Study: AI for Smart Irrigation Systems
* 8.5 Hands-On: Optimizing Irrigation Systems with AI Module 9: AI for Sustainable Cities and Smart Urban Development
* 9.1 AI in Smart City Infrastructure
* 9.2 Sustainable Mobility and Transportation
* 9.3 AI in Urban Resource Optimization
* 9.4 Case Study: AI for Urban Air Quality Monitoring
* 9.5 Hands-On: Optimizing Traffic Flow and Reducing Emissions with AI-Driven Smart Traffic Management Tools you will explore
* TensorFlow
* PyTorch
* Python
* Climate Prediction
* AI-Driven Energy Management Systems
* AI-Based Resource Optimization Tools
* Machine Learning for Waste Reduction
* Smart Grid Optimization Software
* Environmental Data Visualization Platforms
* Sustainability Analytics Frameworks
* AI for Biodiversity Conservation
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€200
E-Learning
max 999
1 dag
AI+ Cloud Practitioner™
Formerly known as AI+ Cloud™
Transform Cloud Computing with Cutting-Edge AI integration
* Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments
* Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies
* Capstone Project: Gain hands-on experience with real-world applications
* Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation
Course Overview
* Course Introduction Preview Module 1: Fundamentals of Artificial Intelligence (AI) and Cloud
* 1.1 Introduction to AI and Its Application
* 1.2 Overview of Cloud Computing and Its Benefits
* 1.3 Benefits and Challenges of AI-Cloud Integration Module 2: Introduction to Artificial Intelligence
* 2.1 Basic Concepts and Principles of AI
* 2.2 Machine Learning and Its Applications
* 2.3 Overview of Common AI Algorithms
* 2.4 Introduction to Python Programming for AI Module 3: Fundamentals of Cloud Computing
* 3.1 Cloud Service Models
* 3.2 Cloud Deployment Models
* 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud) Module 4: AI Services in the Cloud
* 4.1 Integration of AI Services in Cloud Platform
* 4.2 Working with Pre-built Machine Learning Models
* 4.3 Introduction to Cloud-based AI tools Module 5: AI Model Development in the Cloud
* 5.1 Building and Training Machine Learning Models
* 5.2 Model Optimization and Evaluation
* 5.3 Collaborative AI Development in a Cloud Environment Module 6: Cloud Infrastructure for AI
* 6.1 Setting Up and Configuring Cloud Resources
* 6.2 Scalability and Performance Considerations
* 6.3 Data Storage and Management in the Cloud Module 7: Deployment and Integration
* 7.1 Strategies for Deploying AI Models in the Cloud
* 7.2 Integration of AI Solutions with Existing Cloud-Based Applications
* 7.3 API Usage and Considerations Module 8: Future Trends in AI+ Cloud Practitioner™ Integration
* 8.1 Introduction to Future Trends
* 8.2 AI Trends Impacting Cloud Integration Module 9: Capstone Project
* 9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem Optional Module: AI Agents for Cloud Computing
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* TensorFlow
* SHAP (SHapley Additive exPlanations)
* Amazon S3
* AWS SageMaker
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.450
Klassikaal
max 12
5 dagen
AI+ Cloud Practitioner™ eLearning
Formerly known as AI+ Cloud™
Transform Cloud Computing with Cutting-Edge AI integration
* Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments
* Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies
* Capstone Project: Gain hands-on experience with real-world applications
* Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation
Course Overview
* Course Introduction Preview Module 1: Fundamentals of Artificial Intelligence (AI) and Cloud
* 1.1 Introduction to AI and Its Application
* 1.2 Overview of Cloud Computing and Its Benefits
* 1.3 Benefits and Challenges of AI-Cloud Integration Module 2: Introduction to Artificial Intelligence
* 2.1 Basic Concepts and Principles of AI
* 2.2 Machine Learning and Its Applications
* 2.3 Overview of Common AI Algorithms
* 2.4 Introduction to Python Programming for AI Module 3: Fundamentals of Cloud Computing
* 3.1 Cloud Service Models
* 3.2 Cloud Deployment Models
* 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud) Module 4: AI Services in the Cloud
* 4.1 Integration of AI Services in Cloud Platform
* 4.2 Working with Pre-built Machine Learning Models
* 4.3 Introduction to Cloud-based AI tools Module 5: AI Model Development in the Cloud
* 5.1 Building and Training Machine Learning Models
* 5.2 Model Optimization and Evaluation
* 5.3 Collaborative AI Development in a Cloud Environment Module 6: Cloud Infrastructure for AI
* 6.1 Setting Up and Configuring Cloud Resources
* 6.2 Scalability and Performance Considerations
* 6.3 Data Storage and Management in the Cloud Module 7: Deployment and Integration
* 7.1 Strategies for Deploying AI Models in the Cloud
* 7.2 Integration of AI Solutions with Existing Cloud-Based Applications
* 7.3 API Usage and Considerations Module 8: Future Trends in AI+ Cloud Practitioner™ Integration
* 8.1 Introduction to Future Trends
* 8.2 AI Trends Impacting Cloud Integration Module 9: Capstone Project
* 9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem Optional Module: AI Agents for Cloud Computing
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* TensorFlow
* SHAP (SHapley Additive exPlanations)
* Amazon S3
* AWS SageMaker
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€510
E-Learning
max 999
5 dagen