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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™ eLearning
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™
Virtueel
di 9 jun. 2026
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™ 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™
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-4 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™ eLearning
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-4 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™
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 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™ eLearning
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 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
AI+ Data™
's-Hertogenbosch
ma 12 okt. 2026
Mastering AI, Maximizing Data: Your Path to Innovation
* Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
* Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics
* Capstone Application: Solve real-world problems like employee attrition with AI
* Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship
Course Overview
* Course Introduction Preview Module 1: Foundations of Data Science
* 1.1 Introduction to Data Science
* 1.2 Data Science Life Cycle
* 1.3 Applications of Data Science Module 2: Foundations of Statistics
* 2.1 Basic Concepts of Statistics
* 2.2 Probability Theory
* 2.3 Statistical Inference Module 3: Data Sources and Types
* 3.1 Types of Data
* 3.2 Data Sources
* 3.3 Data Storage Technologies Module 4: Programming Skills for Data Science
* 4.1 Introduction to Python for Data Science
* 4.2 Introduction to R for Data Science Module 5: Data Wrangling and Preprocessing
* 5.1 Data Imputation Techniques
* 5.2 Handling Outliers and Data Transformation Module 6: Exploratory Data Analysis (EDA)
* 6.1 Introduction to EDA
* 6.2 Data Visualization Module 7: Generative AI Tools for Deriving Insights
* 7.1 Introduction to Generative AI Tools
* 7.2 Applications of Generative AI Module 8: Machine Learning
* 8.1 Introduction to Supervised Learning Algorithms
* 8.2 Introduction to Unsupervised Learning
* 8.3 Different Algorithms for Clustering
* 8.4 Association Rule Learning with Implementation Module 9: Advance Machine Learning
* 9.1 Ensemble Learning Techniques
* 9.2 Dimensionality Reduction
* 9.3 Advanced Optimization Techniques Module 10: Data-Driven Decision-Making
* 10.1 Introduction to Data-Driven Decision Making
* 10.2 Open Source Tools for Data-Driven Decision Making
* 10.3 Deriving Data-Driven Insights from Sales Dataset Module 11: Data Storytelling
* 11.1 Understanding the Power of Data Storytelling
* 11.2 Identifying Use Cases and Business Relevance
* 11.3 Crafting Compelling Narratives
* 11.4 Visualizing Data for Impact Module 12: Capstone Project - Employee Attrition Prediction
* 12.1 Project Introduction and Problem Statement
* 12.2 Data Collection and Preparation
* 12.3 Data Analysis and Modeling
* 12.4 Data Storytelling and Presentation Optional Module: AI Agents for Data Analysis
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* Google Colab
* MLflow
* Alteryx
* KNIME
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+ Data™ eLearning
Mastering AI, Maximizing Data: Your Path to Innovation
* Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
* Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics
* Capstone Application: Solve real-world problems like employee attrition with AI
* Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship
Course Overview
* Course Introduction Preview Module 1: Foundations of Data Science
* 1.1 Introduction to Data Science
* 1.2 Data Science Life Cycle
* 1.3 Applications of Data Science Module 2: Foundations of Statistics
* 2.1 Basic Concepts of Statistics
* 2.2 Probability Theory
* 2.3 Statistical Inference Module 3: Data Sources and Types
* 3.1 Types of Data
* 3.2 Data Sources
* 3.3 Data Storage Technologies Module 4: Programming Skills for Data Science
* 4.1 Introduction to Python for Data Science
* 4.2 Introduction to R for Data Science Module 5: Data Wrangling and Preprocessing
* 5.1 Data Imputation Techniques
* 5.2 Handling Outliers and Data Transformation Module 6: Exploratory Data Analysis (EDA)
* 6.1 Introduction to EDA
* 6.2 Data Visualization Module 7: Generative AI Tools for Deriving Insights
* 7.1 Introduction to Generative AI Tools
* 7.2 Applications of Generative AI Module 8: Machine Learning
* 8.1 Introduction to Supervised Learning Algorithms
* 8.2 Introduction to Unsupervised Learning
* 8.3 Different Algorithms for Clustering
* 8.4 Association Rule Learning with Implementation Module 9: Advance Machine Learning
* 9.1 Ensemble Learning Techniques
* 9.2 Dimensionality Reduction
* 9.3 Advanced Optimization Techniques Module 10: Data-Driven Decision-Making
* 10.1 Introduction to Data-Driven Decision Making
* 10.2 Open Source Tools for Data-Driven Decision Making
* 10.3 Deriving Data-Driven Insights from Sales Dataset Module 11: Data Storytelling
* 11.1 Understanding the Power of Data Storytelling
* 11.2 Identifying Use Cases and Business Relevance
* 11.3 Crafting Compelling Narratives
* 11.4 Visualizing Data for Impact Module 12: Capstone Project - Employee Attrition Prediction
* 12.1 Project Introduction and Problem Statement
* 12.2 Data Collection and Preparation
* 12.3 Data Analysis and Modeling
* 12.4 Data Storytelling and Presentation Optional Module: AI Agents for Data Analysis
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* Google Colab
* MLflow
* Alteryx
* KNIME
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