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56.691 resultaten

AI+ Foundation™

Nieuwegein ma 7 sep. 2026 en 8 andere data
De toekomst vormgeven met AI: een stevig fundament voor de innovators van morgen * Cursus voor beginners: het ideale startpunt om de basisprincipes en praktische toepassingen van AI te ontdekken * Basisbeginselen: behandelt de grondbeginselen van AI, praktische toepassingen en ethische aspecten * Geen technische achtergrond vereist: vereist alleen een basisinteresse in het gebruik van technologie Module 1: Inleiding tot AI en de impact ervan * 1.1 Inzicht in AI * 1.2 AI in het dagelijks leven * 1.3 Inleiding tot machine learning * 1.4 Use-case, casestudy en praktische oefening Module 2: Prompt Engineering: Interactie met AI * 2.1 Definitie van prompt engineering en het belang ervan * 2.2 Verbetering door middel van goed opgestelde prompts * 2.3 GPT-4 verkennen * 2.4 Use-case, casestudy en praktische oefening Module 3: AI in bedrijfs- en industriële toepassingen * 3.1 Het opstellen van AI-strategieën * 3.2 Belangrijke AI-tools voor het bedrijfsleven * 3.3 De kracht van GAN-AI * 3.4 Use-case, casestudy en praktische oefening Module 4: AI-ethiek en vooringenomenheid * 4.1 Overzicht van ethische overwegingen bij AI * 4.2 Gegevensverzameling en de gevolgen van vooringenomenheid * 4.3 De impact van vooringenomenheid op besluitvorming * 4.4 Use-case, casestudy en praktische oefening Module 5: De toekomst van AI en carrièremogelijkheden * 5.1 De rol van AI bij het aanpakken van mondiale uitdagingen * 5.2 Hoe AI industrieën en de aard van werk hervormt * 5.3 Banenverlies versus het creëren van nieuwe banen * 5.4 Vaardigheden van de toekomst * 5.5 Toepassingsvoorbeelden, casestudy's en praktische oefeningen Tools die je gaat verkennen * ChatGPT * Humata AI * Amto AI * AI-advocaat Inclusief online examen onder toezicht, met één gratis herkansing. Examenopzet: 50 vragen, 70% vereist om te slagen, 90 minuten, online examen onder toezicht Toegang tot alle materialen en examens wordt gedurende 365 dagen na levering verleend. Klassikale cursus + Officieel examen + Digitale badge
€495
Klassikaal
max 12
1 dag

AI+ Foundation™

De toekomst vormgeven met AI: een stevig fundament voor de innovators van morgen * Cursus voor beginners: het ideale startpunt om de basisprincipes en praktische toepassingen van AI te ontdekken * Basisbeginselen: behandelt de grondbeginselen van AI, praktische toepassingen en ethische aspecten * Geen technische achtergrond vereist: vereist alleen een basisinteresse in het gebruik van technologie Module 1: Inleiding tot AI en de impact ervan * 1.1 Inzicht in AI * 1.2 AI in het dagelijks leven * 1.3 Inleiding tot machine learning * 1.4 Use-case, casestudy en praktische oefening Module 2: Prompt Engineering: Interactie met AI * 2.1 Definitie van prompt engineering en het belang ervan * 2.2 Verbetering door middel van goed opgestelde prompts * 2.3 GPT-4 verkennen * 2.4 Use-case, casestudy en praktische oefening Module 3: AI in bedrijfs- en industriële toepassingen * 3.1 Het opstellen van AI-strategieën * 3.2 Belangrijke AI-tools voor het bedrijfsleven * 3.3 De kracht van GAN-AI * 3.4 Use-case, casestudy en praktische oefening Module 4: AI-ethiek en vooringenomenheid * 4.1 Overzicht van ethische overwegingen bij AI * 4.2 Gegevensverzameling en de gevolgen van vooringenomenheid * 4.3 De impact van vooringenomenheid op besluitvorming * 4.4 Use-case, casestudy en praktische oefening Module 5: De toekomst van AI en carrièremogelijkheden * 5.1 De rol van AI bij het aanpakken van mondiale uitdagingen * 5.2 Hoe AI industrieën en de aard van werk hervormt * 5.3 Banenverlies versus het creëren van nieuwe banen * 5.4 Vaardigheden van de toekomst * 5.5 Toepassingsvoorbeelden, casestudy's en praktische oefeningen Tools die je gaat verkennen * ChatGPT * Humata AI * Amto AI * AI-advocaat Inclusief online examen onder toezicht, met één gratis herkansing. Examenopzet: 50 vragen, 70% vereist om te slagen, 90 minuten, online examen onder toezicht Toegang tot alle materialen en examens wordt gedurende 365 dagen na levering verleend. Klassikale cursus + Officieel examen + Digitale badge
€0
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
€895
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
€200
E-Learning
max 999
1 dag

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
€895
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
€200
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
€895
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
€200
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
€895
Klassikaal
max 12
1 dag

AI+ Vibe Coding Practitioner™ eLearning

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
€200
E-Learning
max 999
1 dag