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AI+ Everyone Fundamentals™
Nieuwegein
do 10 sep. 2026
en 9 andere data
Geef mensen meer mogelijkheden met AI: toegankelijk, intuïtief, impactvol
Beginnervriendelijke cursus: Ideaal startpunt voor wie nieuw is bij AI, met kernprincipes en praktische inzichten
Uitgebreide leerervaring: AI-basisprincipes, toepassingen uit de echte wereld, generatieve AI en ethische overwegingen
Industriële en maatschappelijke impact: Inzicht krijgen in de rol van AI in sectoren en de bredere maatschappelijke invloed ervan
Praktische richtlijnen: Biedt stapsgewijze hulpmiddelen om je AI-reis een vliegende start te geven en essentiële vaardigheden te ontwikkelen
Examen: 50 vragen, 70% slaagkans, 90 minuten, online toezichtsexamenInstructeurgestuurde cursus of cursus op eigen tempo + officieel examen + digitale badge
€995
Klassikaal
max 12
1 dag
AI+ Everyone Fundamentals™ eLearning
Geef mensen meer mogelijkheden met AI: toegankelijk, intuïtief, impactvol
Beginnervriendelijke cursus: Ideaal startpunt voor wie nieuw is bij AI, met kernprincipes en praktische inzichten
Uitgebreide leerervaring: AI-basisprincipes, toepassingen uit de echte wereld, generatieve AI en ethische overwegingen
Industriële en maatschappelijke impact: Inzicht krijgen in de rol van AI in sectoren en de bredere maatschappelijke invloed ervan
Praktische richtlijnen: Biedt stapsgewijze hulpmiddelen om je AI-reis een vliegende start te geven en essentiële vaardigheden te ontwikkelen
Examen: 50 vragen, 70% slaagkans, 90 minuten, online toezichtsexamenInstructeurgestuurde cursus of cursus op eigen tempo + officieel examen + digitale badge
€225
E-Learning
max 999
1 dag
AI+ Policy Maker Practitioner™
Formerly known as AI+ Policy Maker™
Empower Your Leadership with AI: Master Policy Development and Implementation for the Future
AI-Driven Policy Design: Leverage AI to transform policy creation and improve decision-making efficiency
Ethical Policy Making: Ensure fairness and transparency while integrating AI in responsible policy frameworks
Impact-Centric Frameworks: Create AI-powered policies that drive measurable outcomes and enhance governance efficiency
Module 1: Introduction to Artificial Intelligence
1.1 Understanding AI: Definitions and Concepts
1.2 Historical Development of AI
1.3 Current AI Technologies and Applications
1.4 AI Trends and Future Directions
1.5 AI Terminology and Jargon for Policy Makers
Module 2: AI in Governance and Public Policy
2.1 Role of AI in Government and Public Services
2.2 Case Studies of AI in Public Administration
2.3 AI for Regulatory Compliance and Enforcement
2.4 Challenges of AI Adoption in Government
2.5 Policy Considerations for AI Implementation
Module 3: Ethical, Social, and Human Rights Implications of AI
3.1 Principles of AI Ethics
3.2 Bias, Fairness, and Discrimination in AI Systems
3.3 Privacy and Data Protection
3.4 Socio-Economic Impacts of AI
3.5 AI and Human Rights
Module 4: Legal and Regulatory Frameworks for AI
4.1 Overview of AI Regulations Globally
4.2 Data Governance and Privacy Laws
4.3 Intellectual Property Rights in AI
4.4 Liability and Accountability in AI Systems
4.5 Developing AI Policies and Legislation
Module 5: AI Risk Management and Security
5.1 AI Safety and Security Challenges
5.2 Risk Assessment and Management Strategies
5.3 Cybersecurity and AI
5.4 Ensuring Reliability and Resilience
5.5 Incident Response and Crisis Management
Module 6: Economic Impacts of AI
6.1 AI and the Future of Work
6.2 AI’s Role in Economic Growth
6.3 Supporting AI Innovation and Entrepreneurship
6.4 AI in Developing Economies
6.5 Addressing Economic Inequalities
Module 7: AI Strategy, Implementation, and Collaboration
7.1 Developing National AI Strategies
7.2 Building AI Capabilities in the Public Sector
7.3 Public-Private Partnerships in AI
7.4 Funding and Investment in AI
7.5 Monitoring, Evaluation, and Continuous Improvement
Module 8: Shaping the Future of AI Policy
8.1 Emerging AI Technologies and Trends
8.2 International Cooperation on AI Governance
8.3 AI and the Sustainable Development Goals (SDGs)
8.4 Public Engagement and Transparency
8.5 The Future of AI Policy Making
Optional Module: AI Agents for Policy Maker
1. Understanding AI Agents
2. Case Study
3. Hands-On Activity
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
€995
Klassikaal
max 12
1 dag
AI+ Policy Maker Practitioner™ eLearning
Formerly known as AI+ Policy Maker™
Empower Your Leadership with AI: Master Policy Development and Implementation for the Future
AI-Driven Policy Design: Leverage AI to transform policy creation and improve decision-making efficiency
Ethical Policy Making: Ensure fairness and transparency while integrating AI in responsible policy frameworks
Impact-Centric Frameworks: Create AI-powered policies that drive measurable outcomes and enhance governance efficiency
Module 1: Introduction to Artificial Intelligence
1.1 Understanding AI: Definitions and Concepts
1.2 Historical Development of AI
1.3 Current AI Technologies and Applications
1.4 AI Trends and Future Directions
1.5 AI Terminology and Jargon for Policy Makers
Module 2: AI in Governance and Public Policy
2.1 Role of AI in Government and Public Services
2.2 Case Studies of AI in Public Administration
2.3 AI for Regulatory Compliance and Enforcement
2.4 Challenges of AI Adoption in Government
2.5 Policy Considerations for AI Implementation
Module 3: Ethical, Social, and Human Rights Implications of AI
3.1 Principles of AI Ethics
3.2 Bias, Fairness, and Discrimination in AI Systems
3.3 Privacy and Data Protection
3.4 Socio-Economic Impacts of AI
3.5 AI and Human Rights
Module 4: Legal and Regulatory Frameworks for AI
4.1 Overview of AI Regulations Globally
4.2 Data Governance and Privacy Laws
4.3 Intellectual Property Rights in AI
4.4 Liability and Accountability in AI Systems
4.5 Developing AI Policies and Legislation
Module 5: AI Risk Management and Security
5.1 AI Safety and Security Challenges
5.2 Risk Assessment and Management Strategies
5.3 Cybersecurity and AI
5.4 Ensuring Reliability and Resilience
5.5 Incident Response and Crisis Management
Module 6: Economic Impacts of AI
6.1 AI and the Future of Work
6.2 AI’s Role in Economic Growth
6.3 Supporting AI Innovation and Entrepreneurship
6.4 AI in Developing Economies
6.5 Addressing Economic Inequalities
Module 7: AI Strategy, Implementation, and Collaboration
7.1 Developing National AI Strategies
7.2 Building AI Capabilities in the Public Sector
7.3 Public-Private Partnerships in AI
7.4 Funding and Investment in AI
7.5 Monitoring, Evaluation, and Continuous Improvement
Module 8: Shaping the Future of AI Policy
8.1 Emerging AI Technologies and Trends
8.2 International Cooperation on AI Governance
8.3 AI and the Sustainable Development Goals (SDGs)
8.4 Public Engagement and Transparency
8.5 The Future of AI Policy Making
Optional Module: AI Agents for Policy Maker
1. Understanding AI Agents
2. Case Study
3. Hands-On Activity
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
€225
E-Learning
max 999
1 dag
AI+ Program Director – Practitioner™ eLearning
Master AI Leadership with Practical Program Management
AI Strategy Development: Learn to design and implement AI strategies that align with business goals, driving innovation and performance.
Leading AI Projects: Gain skills in managing AI projects, ensuring timely execution, resource allocation, and effective collaboration.
AI Program Integration: Understand how to integrate AI into business processes for seamless transitions and maximum value.
Managing AI Teams: Lead cross-functional teams, fostering collaboration and driving continuous improvement in AI initiatives.
Future-Proofing AI Programs: Stay ahead of AI trends and adapt strategies to ensure long-term competitiveness in the evolving landscape.
Module 1: Foundations of AI for Program Strategy – Introduction
1.1 Understanding of AI, ML, and Deep Learning
1.2 AI Lifecycle & Real-World Applications
1.3 Societal Impact of AI
1.4 Use Case: Triage System (AI for Emergency Services)
1.5 Case Study: Retail Recommendation System (Personalizing Customer Experience)
1.6 Hands-on: Use Teachable Machine to Build a Simple AI Classifier
Module 2: Identifying AI Opportunities & Use Cases
2.1 Introduce AI Strategy Alignment Frameworks: AI Canvas, Value vs Feasibility Matrix
2.2 Signs That a Process May Benefit from AI: Repetitive Tasks, Data-Rich Environments, Personalization Needs
2.3 Prioritization Techniques: Weighted Scoring, Risk-Adjusted ROI
2.4 Use-Case: Financial AI – Fraud Detection Systems Using AI
2.5 Case Study: AI-Driven Project Management System for a Program Director
2.6 Hands-on: Use Trello to Create a Board and Prioritize AI Opportunities Within a Given Scenario
Module 3: Governance & Ethics in AI
3.1 Responsible AI Principles
3.2 AI Bias & Risk Mitigation
3.3 Use-case: Auditing Bias in AI-Powered Recruitment to Ensure Fair Hiring
3.4 Case Study: Mitigating Algorithmic Bias in Credit Scoring Models to Ensure Fair Lending Practices
3.5 Hands-on: Use Google’s What-If Tool in Google Colab to Evaluate Model Fairness and Bias
Module 4: AI Project Lifecycle & Integration
4.1 AI Project Planning & CRISP-DM
4.2 Integration: Build vs Buy vs Partner
4.3 AI Project Management Tools
4.4 Use Cases: AI for Predictive Maintenance (Asset Management in Manufacturing)
4.5 Tool-Based Hands-on Activity: Simulate an AI Project in Asana
Module 5: Data Strategy & Infrastructure for AI
5.1 Data Governance & Quality
5.2 Setting up Data Pipelines for AI
5.3 Sensitive Data Management
5.4 Use Case: Retail Inventory System — AI-driven Restocking and Demand Prediction
5.5 Case Study: Healthcare Data Security — Managing Patient Privacy in AI-Based Healthcare Systems
5.6 Tool-Based Hands-on Activity: Set up Airbyte Cloud and Build a Basic Data Pipeline
Module 6: AI Integration — Build vs Buy vs Partner
6.1 Evaluating AI Solutions
6.2 Vendor Evaluation & Management
6.3 Use Case: AI Vendor Selection — Choosing Predictive Maintenance Solutions for a Manufacturing Plant
6.4 Tool-Based Hands-on Activity: Use a Vendor Selection Template to Evaluate AI Vendors (Google Sheets)
Module 7: AI Risk Management & Compliance
7.1 Regulatory Frameworks
7.2 Bias Detection & Mitigation
7.3 Use Case: Facial Recognition Bias (Law Enforcement Systems)
7.4 Case Study: AI in Finance: Ensuring Compliance in AI Deployments
7.5 Tool-Based Hands-on Activity: Bias Testing & Fairness Evaluation Using KNIME and Google PAIR Facets Fairness Explorer
Module 8: AI Tools & Techniques for Project Management
8.1 AI Project Management Tools
8.2 Data Management Tools
8.3 Case Study and Use Case: AI Workflow Management: Using project management tools for AI deployment in the retail sector
8.4 Tool-Based Hands-on Activity: Use Asana to simulate project timelines, setting up tasks and milestones for an AI initiative
Module 9: Leadership in AI
9.1 Leading AI Teams & Change Management
9.2 Managing Stakeholders & Communication
9.3 Use Case: AI in Manufacturing: Leading AI Implementation in a Large-Scale Manufacturing Operation
9.4 Tool-Based Hands-on Activity: Use Miro to Map Stakeholder Communication Strategies and Identify Key Influencers
Module 10: Scaling AI Initiatives
10.1 From Pilot to Full-Scale Deployment
10.2 Organizational Maturity Models for AI
10.3 Use Case: Scaling AI in Retail: Expanding AI-driven Recommendations Globally
10.4 Tool-Based Hands-on Activity: Create a Scaling Roadmap Using Lucidchart Outlining Key steps in Scaling AI Initiatives.
Module 11: Future Trends in AI
11.1 Emerging AI Technologies
11.2 Use Case / Case Study: AI in Autonomous Vehicles: The future of AI in self-driving cars
11.3 Tool-Based Hands-on Activity: Explore Hugging Face Transformers for NLP and TensorFlow for Deep Learning Applications
Module 12: Capstone Project & Presentation
12.1 Capstone Project Overview
12.2 Presentation & Feedback
12.3 Final Review & Certification – Method, Process, and Feedback Mechanism
Tools you will explore
Microsoft Project
JIRA
Trello
Asana
Monday.com
Basecamp
Wrike
ClickUp
GitLab
Confluence
Smartsheet
Slack
Power BI
Tableau
Azure DevOps
AWS CloudFormation
Google Cloud AI Platform
TIBCO Jaspersoft
RapidMiner
Minitab
Balsamiq
Miro
Zoom
Jenkins
Salesforce
Lucidchart
ServiceNow
Redmine
Airtable
Workfront
Notion
QlikView
Klipfolio
Hootsuite
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
AI+ Doctor Practitioner™
Nieuwegein
vr 18 sep. 2026
en 1 andere data
Formerly known as AI+ Doctor™
Redefining Healthcare with AI-Driven Diagnosis
Clinical Intelligence Focus: Designed for medical professionals to integrate AI into patient care and diagnostics
Data-Driven Decisions: Equips doctors with tools to interpret AI-generated insights for precise treatment planning
Comprehensive Medical AI Knowledge: Covers AI applications from predictive analytics to medical imaging and virtual health
Future-Ready Expertise: Empowers healthcare practitioners to lead AI-driven innovations in clinical practice
Module 1: What is AI for Doctors?
1.1 From Decision Support to Diagnostic Intelligence
1.2 What Makes AI in Medicine Unique?
1.3 Types of Machine Learning in Medicine
1.4 Common Algorithms and What They Do in Healthcare
1.5 Real-World Use Cases Across Medical Specialties
1.6 Debunking Myths About AI in Healthcare
1.7 Real Tools in Use by Clinicians Today
1.8 Hands-on: Medical Imaging Analysis using MediScan AI
Module 2: AI in Diagnostics & Imaging
2.1 Introduction to Neural Networks: Unlocking the Power of AI
2.2 Convolutional Neural Networks (CNNs) for Visual Data: Seeing with AI’s Eyes
2.3 Image Modalities in Medical AI: AI’s Multi-Modal Vision
2.4 Model Training Workflow: From Data Labeling to Deployment – The AI Lifecycle in Medicine
2.5 Human-AI Collaboration in Diagnosis: The Power of Augmented Intelligence
2.6 FDA-Approved AI Tools in Diagnostic Imaging: Trust and Validation
2.7 Hands-on Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
Module 3: Introduction to Fundamental Data Analysis
3.1 Understanding Clinical Data Types – EHRs, Vitals, Lab Results
3.2 Structured vs. Unstructured Data in Medicine
3.3 Role of Dashboards and Visualization in Clinical Decisions
3.4 Pattern Recognition and Signal Detection in Patient Data
3.5 Identifying At-Risk Patients via Trends and AI Scores
3.6 Interactive Activity: AI Assistant for Clinical Note Insights
Module 4: Predictive Analytics & Clinical Decision Support – Empowering Proactive Patient Care
4.1 Predictive Models for Risk Stratification – Sepsis and Hospital Readmissions
4.2 Logistic Regression, Decision Trees, Ensemble Models
4.3 Real-Time Alerts – Early Warning Systems (MEWS, NEWS)
4.4 Sensitivity vs. Specificity – Metric Choice by Clinical Need
4.5 ICU and ER Use Cases for AI-Triggered Interventions
Module 5: NLP and Generative AI in Clinical Use
5.1 Foundations of NLP in Healthcare
5.2 Large Language Models (LLMs) in Medicine
5.3 Prompt Engineering in Clinical Contexts
5.4 Generative AI Use Cases – Summarization, Counselling Scripts, Translation
5.5 Ambient Intelligence: Next-Gen Clinical Documentation
5.6 Limitations & Risks of NLP and Generative AI in Medicine
5.7 Case Study: Transforming Clinical Documentation and Enhancing Patient Care with Nabla Copilot
Module 6: Ethical and Equitable AI Use
6.1 Algorithmic Bias – Race, Gender, Socioeconomic Impact
6.2 Explainability and Transparency (SHAP and LIME)
6.3 Validating AI Across Populations
6.4 Regulatory Standards – HIPAA, GDPR, FDA/EMA Compliance
6.5 Drafting Ethical AI Use Policies
6.6 Case Study – Biased Pulse Oximetry Detection
Module 7: Evaluating AI Tools in Practice
7.1 Core Metrics: Understanding the Basics
7.2 Confusion Matrix & ROC Curve Interpretation
7.3 Metric Matching by Clinical Context
7.4 Interpreting AI Outputs: Enhancing Clinical Decision-Making
7.5 Critical Evaluation of Vendor Claims: Ensuring Reliability and Effectiveness
7.6 Red Flags in Commercial AI Tools: Recognizing and Mitigating Risks
7.7 Checklist: “10 Questions to Ask Before Buying AI Tools”
7.8 Hands-on
Module 8: Implementing AI in Clinical Settings
8.1 Identifying Department-Specific AI Use Cases
8.2 Mapping AI to Workflows (Pre-diagnosis, Treatment, Follow-up)
8.3 Pilot Planning: Timeline, Data, Feedback Cycles
8.4 Team Roles – Clinical Champion, AI Specialist, IT Admin
8.5 Monitoring AI Errors – Root Cause Analysis
8.6 Change Management in Clinical Teams
8.7 Example: ER Workflow with Triage AI Integration
8.8 Scaling AI Solutions Across the Healthcare System
8.9 Evaluating AI Impact and Performance Post-Deployment
Tools you will explore
Python
TensorFlow
Scikit-learn
Keras
Hugging Face Transformers
Jupyter Notebooks
Tableau
Matplotlib
SQL
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+ Doctor Practitioner™ eLearning
Formerly known as AI+ Doctor™
Redefining Healthcare with AI-Driven Diagnosis
Clinical Intelligence Focus: Designed for medical professionals to integrate AI into patient care and diagnostics
Data-Driven Decisions: Equips doctors with tools to interpret AI-generated insights for precise treatment planning
Comprehensive Medical AI Knowledge: Covers AI applications from predictive analytics to medical imaging and virtual health
Future-Ready Expertise: Empowers healthcare practitioners to lead AI-driven innovations in clinical practice
Module 1: What is AI for Doctors?
1.1 From Decision Support to Diagnostic Intelligence
1.2 What Makes AI in Medicine Unique?
1.3 Types of Machine Learning in Medicine
1.4 Common Algorithms and What They Do in Healthcare
1.5 Real-World Use Cases Across Medical Specialties
1.6 Debunking Myths About AI in Healthcare
1.7 Real Tools in Use by Clinicians Today
1.8 Hands-on: Medical Imaging Analysis using MediScan AI
Module 2: AI in Diagnostics & Imaging
2.1 Introduction to Neural Networks: Unlocking the Power of AI
2.2 Convolutional Neural Networks (CNNs) for Visual Data: Seeing with AI’s Eyes
2.3 Image Modalities in Medical AI: AI’s Multi-Modal Vision
2.4 Model Training Workflow: From Data Labeling to Deployment – The AI Lifecycle in Medicine
2.5 Human-AI Collaboration in Diagnosis: The Power of Augmented Intelligence
2.6 FDA-Approved AI Tools in Diagnostic Imaging: Trust and Validation
2.7 Hands-on Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
Module 3: Introduction to Fundamental Data Analysis
3.1 Understanding Clinical Data Types – EHRs, Vitals, Lab Results
3.2 Structured vs. Unstructured Data in Medicine
3.3 Role of Dashboards and Visualization in Clinical Decisions
3.4 Pattern Recognition and Signal Detection in Patient Data
3.5 Identifying At-Risk Patients via Trends and AI Scores
3.6 Interactive Activity: AI Assistant for Clinical Note Insights
Module 4: Predictive Analytics & Clinical Decision Support – Empowering Proactive Patient Care
4.1 Predictive Models for Risk Stratification – Sepsis and Hospital Readmissions
4.2 Logistic Regression, Decision Trees, Ensemble Models
4.3 Real-Time Alerts – Early Warning Systems (MEWS, NEWS)
4.4 Sensitivity vs. Specificity – Metric Choice by Clinical Need
4.5 ICU and ER Use Cases for AI-Triggered Interventions
Module 5: NLP and Generative AI in Clinical Use
5.1 Foundations of NLP in Healthcare
5.2 Large Language Models (LLMs) in Medicine
5.3 Prompt Engineering in Clinical Contexts
5.4 Generative AI Use Cases – Summarization, Counselling Scripts, Translation
5.5 Ambient Intelligence: Next-Gen Clinical Documentation
5.6 Limitations & Risks of NLP and Generative AI in Medicine
5.7 Case Study: Transforming Clinical Documentation and Enhancing Patient Care with Nabla Copilot
Module 6: Ethical and Equitable AI Use
6.1 Algorithmic Bias – Race, Gender, Socioeconomic Impact
6.2 Explainability and Transparency (SHAP and LIME)
6.3 Validating AI Across Populations
6.4 Regulatory Standards – HIPAA, GDPR, FDA/EMA Compliance
6.5 Drafting Ethical AI Use Policies
6.6 Case Study – Biased Pulse Oximetry Detection
Module 7: Evaluating AI Tools in Practice
7.1 Core Metrics: Understanding the Basics
7.2 Confusion Matrix & ROC Curve Interpretation
7.3 Metric Matching by Clinical Context
7.4 Interpreting AI Outputs: Enhancing Clinical Decision-Making
7.5 Critical Evaluation of Vendor Claims: Ensuring Reliability and Effectiveness
7.6 Red Flags in Commercial AI Tools: Recognizing and Mitigating Risks
7.7 Checklist: “10 Questions to Ask Before Buying AI Tools”
7.8 Hands-on
Module 8: Implementing AI in Clinical Settings
8.1 Identifying Department-Specific AI Use Cases
8.2 Mapping AI to Workflows (Pre-diagnosis, Treatment, Follow-up)
8.3 Pilot Planning: Timeline, Data, Feedback Cycles
8.4 Team Roles – Clinical Champion, AI Specialist, IT Admin
8.5 Monitoring AI Errors – Root Cause Analysis
8.6 Change Management in Clinical Teams
8.7 Example: ER Workflow with Triage AI Integration
8.8 Scaling AI Solutions Across the Healthcare System
8.9 Evaluating AI Impact and Performance Post-Deployment
Tools you will explore
Python
TensorFlow
Scikit-learn
Keras
Hugging Face Transformers
Jupyter Notebooks
Tableau
Matplotlib
SQL
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+ Nurse Practitioner™
Nieuwegein
vr 4 sep. 2026
en 1 andere data
Formerly known as AI+ Nurse™
Blending Human Touch with AI Intelligence
Patient-Centric AI Care: Designed for nurses to leverage AI for enhanced patient outcomes
Data-Driven Decisions: Provides practical insights for informed clinical and operational choices
Comprehensive AI Understanding: Covers AI fundamentals to real-world healthcare applications
Clinical Excellence with AI: Empowers nurses to confidently integrate AI into daily healthcare practice
Module 1: What is AI for Nurses?
1.1 What is AI for Nurses?
1.2 Where AI Shows Up in Nursing
1.3 Case Study: Improving Patient Safety and Nursing Efficiency with AI at Riverside Medical Center
1.4 Hands-on: Using Nurse AI for Clinical Data Visualization in Postoperative Nursing Care
Module 2: AI for Documentation, Workflow, and Data Literacy
2.1 Introduction to Natural Language Processing
2.2 Workflow Automation: Transforming Nursing Practice
2.3 Beginner’s Guide to Data Literacy in Nursing
2.4 Legal & Compliance Basics in Nursing AI Documentation
2.5 Case Study: Integrating AI and Workflow Automation at Massachusetts General Hospital (MGH)
2.6 Hands-On Exercise: Using the ChatGPT Registered Nurse Tool in Clinical Documentation and Patient Education
Module 3: Predictive AI and Patient Safety
3.1 Understanding Predictive Models
3.2 Alert Fatigue and Trust
3.3 Simulation Activity: Responding to Real-Time Deterioration Alerts
3.4 Collaborating Across Teams
3.5 Bias in Predictions
3.6 Case Study
3.7 Hands-on Activity: Interpreting Predictive Alerts with ChatGPT
Module 4: Generative AI in Nursing
4.1 Introduction to Generative AI in Nursing
4.2 Large Language Models (LLMs) for Nurses
4.3 Creating Patient Education Materials with AI
4.4 Ensuring Safe and Ethical Use of AI
4.5 Case Study
4.6 Hands-On Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
Module 5: Ethics, Safety, and Advocacy in AI Integration
5.1 Bias, Fairness, and Inclusion
5.2 Informed Consent and Transparency
5.3 Nurse Advocacy and Professional Responsibilities
5.4 Creating an Ethics Checklist
5.5 Stakeholder Feedback Techniques
5.6 Legal and Regulatory Considerations
5.7 Psychological and Social Implications
5.8 Case Study: Addressing Racial Bias in Healthcare Algorithms (Optum Algorithm Case).
5.9 Hands-on: Uncovering Bias in Diabetes Risk Prediction: A Fairness Audit Using Aequitas
Module 6: Evaluating and Selecting AI Tools
6.1 Understanding Performance Metrics
6.2 Vendor Red Flags
6.3 Nurse Role in Selection
6.4 Evaluation Templates and Checklists
6.5 Use Cases: AI in Clinical Decision-Making
6.6 Case Study: Using AI to Enhance Real-Time Clinical Decision-Making at UAB Medicine with MIC Sickbay
6.7 Hands-on: Evaluating AI Diagnostic Model Performance Using Confusion Matrix Metrics
Module 7: Implementing AI and Leading Change on the Unit
7.1 Building Buy-In: Promoting AI as an Ally, Not a Competitor
7.2 Change Management Essentials
7.3 Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
7.4 Monitoring Quality Improvement: Leveraging AI Metrics for Continuous Enhancement
7.5 Error Reporting and Safety Protocols: Ensuring Safe and Reliable AI Integration
7.6 Hands-On Activity: Calculating Clinical Risk Scores and Visualization with ChatGPT
Module 8: Capstone Project
1. Capstone Project – Designing a Personal AI-in-Nursing Impact Plan
Tools you will explore
Python
Scikit-learn
Keras
Jupyter Notebooks
Matplotlib
Power BI
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+ Nurse Practitioner™ eLearning
Formerly known as AI+ Nurse™
Blending Human Touch with AI Intelligence
Patient-Centric AI Care: Designed for nurses to leverage AI for enhanced patient outcomes
Data-Driven Decisions: Provides practical insights for informed clinical and operational choices
Comprehensive AI Understanding: Covers AI fundamentals to real-world healthcare applications
Clinical Excellence with AI: Empowers nurses to confidently integrate AI into daily healthcare practice
Module 1: What is AI for Nurses?
1.1 What is AI for Nurses?
1.2 Where AI Shows Up in Nursing
1.3 Case Study: Improving Patient Safety and Nursing Efficiency with AI at Riverside Medical Center
1.4 Hands-on: Using Nurse AI for Clinical Data Visualization in Postoperative Nursing Care
Module 2: AI for Documentation, Workflow, and Data Literacy
2.1 Introduction to Natural Language Processing
2.2 Workflow Automation: Transforming Nursing Practice
2.3 Beginner’s Guide to Data Literacy in Nursing
2.4 Legal & Compliance Basics in Nursing AI Documentation
2.5 Case Study: Integrating AI and Workflow Automation at Massachusetts General Hospital (MGH)
2.6 Hands-On Exercise: Using the ChatGPT Registered Nurse Tool in Clinical Documentation and Patient Education
Module 3: Predictive AI and Patient Safety
3.1 Understanding Predictive Models
3.2 Alert Fatigue and Trust
3.3 Simulation Activity: Responding to Real-Time Deterioration Alerts
3.4 Collaborating Across Teams
3.5 Bias in Predictions
3.6 Case Study
3.7 Hands-on Activity: Interpreting Predictive Alerts with ChatGPT
Module 4: Generative AI in Nursing
4.1 Introduction to Generative AI in Nursing
4.2 Large Language Models (LLMs) for Nurses
4.3 Creating Patient Education Materials with AI
4.4 Ensuring Safe and Ethical Use of AI
4.5 Case Study
4.6 Hands-On Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
Module 5: Ethics, Safety, and Advocacy in AI Integration
5.1 Bias, Fairness, and Inclusion
5.2 Informed Consent and Transparency
5.3 Nurse Advocacy and Professional Responsibilities
5.4 Creating an Ethics Checklist
5.5 Stakeholder Feedback Techniques
5.6 Legal and Regulatory Considerations
5.7 Psychological and Social Implications
5.8 Case Study: Addressing Racial Bias in Healthcare Algorithms (Optum Algorithm Case).
5.9 Hands-on: Uncovering Bias in Diabetes Risk Prediction: A Fairness Audit Using Aequitas
Module 6: Evaluating and Selecting AI Tools
6.1 Understanding Performance Metrics
6.2 Vendor Red Flags
6.3 Nurse Role in Selection
6.4 Evaluation Templates and Checklists
6.5 Use Cases: AI in Clinical Decision-Making
6.6 Case Study: Using AI to Enhance Real-Time Clinical Decision-Making at UAB Medicine with MIC Sickbay
6.7 Hands-on: Evaluating AI Diagnostic Model Performance Using Confusion Matrix Metrics
Module 7: Implementing AI and Leading Change on the Unit
7.1 Building Buy-In: Promoting AI as an Ally, Not a Competitor
7.2 Change Management Essentials
7.3 Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
7.4 Monitoring Quality Improvement: Leveraging AI Metrics for Continuous Enhancement
7.5 Error Reporting and Safety Protocols: Ensuring Safe and Reliable AI Integration
7.6 Hands-On Activity: Calculating Clinical Risk Scores and Visualization with ChatGPT
Module 8: Capstone Project
1. Capstone Project – Designing a Personal AI-in-Nursing Impact Plan
Tools you will explore
Python
Scikit-learn
Keras
Jupyter Notebooks
Matplotlib
Power BI
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+ Vibe Coding Practitioner™
's-Hertogenbosch
vr 27 nov. 2026
Supercharge coding with AI+ Vibe Coding Practitioner™ for smarter, faster creation
Beginner-Friendly Approach: Designed for aspiring creators eager to explore AI-assisted coding with ease and confidence
Interactive Learning Journey: Blends core coding concepts, intuitive AI tools, and hands-on practice to build real problem-solving skills
Project-Driven Growth: Provides guided exercises and practical projects to help you build, refine, and showcase your AI-powered coding talents
Module 1: Introduction to Vibe Coding & AI Tools
1.1 What is Vibe Coding?
1.2 Evolution of AI in Software Development – Low Code vs No Code vs Vibe Coding
1.3 Overview of Common AI Coding Tools by Functionality
1.4 SDLC for a Vibe Coding Product
1.5 Hands-on Lab: Familiarizing Learners with Multiple AI Coding Tools
1.6 Case Studies
Module 2: Prompting for Code – Basics & Best Practices
2.1 Anatomy of a Good Prompt
2.2 Prompt Types – Instructive, Descriptive, Iterative
2.3 Prompting Patterns – Zero-Shot, Few-Shot, Chain-of-Thought
2.4 Hands-on Lab: Practice Zero-Shot, Few-Shot, and Chain-of-Thought Prompting
2.5 Use-Case 1: Creating a Python Calculator
2.6 Use-Case 2: Optimizing AI-generated Code Using Different Prompt Types
Module 3: Debugging & Testing via AI
3.1 Reviewing and Refining AI-generated Code
3.2 Prompting for Bug Fixes and Test Coverage
3.3 Using AI-generated Unit Testing
3.4 Detecting Hallucinations and Unsafe Code
3.5 Hands-on Lab: AI-Assisted Debugging and Unit Testing
3.6 Activity Section
Module 4: Building a Simple Full-Stack App with Prompts
4.1 Planning the App: Frontend + Backend
4.2 Using IDEs and Code Generators to Scaffold Code
4.3 Connecting Components Using Natural Language
4.4 Deploying and Testing the MVP in Simulated Environment
4.5 Hands-on Lab: Building and Connecting the Frontend and Backend for Contact Form Submission
4.6 Hands-on Lab: Building a Standalone Desktop Calculator Application Using Tkinter
4.7 Hands-on Assignment 1: Task Management System – Full-Stack Development Using Prompts
Module 5: Code Ethics, Security, and AI Limits
5.1 AI Limitations and Biases
5.2 Prompt Injection and Mitigation Strategies
5.3 Data Privacy and Secure Coding
5.4 Responsible Use of AI in Production
5.5 Hands-on Lab: Build Awareness of AI Limitations and Responsible Practices
Module 6: Capstone Project – Prompt-Driven App
6.1 Apply All Learned Skills in a Real-World Project
6.2 Collaborate and Iterate Using AI Tools
6.3 Demonstrate End-to-End Development Using Prompts
6.4 Capstone Project Use Case: AI-Powered To-Do List Application
6.5 Capstone Project Use Case: AI-Powered Note-Taking Desktop App
6.6 Assignments
Tools you will explore
Python
TensorFlow
PyTorch
GitHub Copilot
OpenAI Codex
Hugging Face Hub
LangChain
FastAPI
VS Code
Jupyter Notebooks
Pandas
NumPy
Scikit-learn
Docker
Streamlit
API Integration Tools
Prompt Engineering Frameworks
Automation SDKs
Version Control Systems (Git)
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