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AI+ Video™
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
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
€995
Klassikaal
max 12
1 dag
AI+ Video™ 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
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
€225
E-Learning
max 999
1 dag
AI+ Supply Chain Practitioner™
Formerly known as AI+ Supply Chain™ <br> <br> 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
€995
Klassikaal
max 12
1 dag
AI+ Supply Chain Practitioner™ eLearning
Formerly known as AI+ Supply Chain™ <br> <br> 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
€225
E-Learning
max 999
1 dag
AI+ Chief AI Officer Practitioner™
Formerly known as AI+ Chief AI Officer™ <br> <br> 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
€995
Klassikaal
max 12
1 dag
AI+ Chief AI Officer Practitioner™ eLearning
Formerly known as AI+ Chief AI Officer™ <br> <br> 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
€225
E-Learning
max 999
1 dag
AI+ Sustainability Practitioner™
Formerly known as AI+ Sustainability™<br> <br> 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
€995
Klassikaal
max 12
1 dag
AI+ Sustainability Practitioner™ eLearning
Formerly known as AI+ Sustainability™<br> <br> 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
€225
E-Learning
max 999
1 dag
AI+ Cloud Practitioner™
Formerly known as AI+ Cloud™<br><br>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
€3.930
Klassikaal
max 12
5 dagen
AI+ Cloud Practitioner™ eLearning
Formerly known as AI+ Cloud™<br><br>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
€530
E-Learning
max 999
5 dagen