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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
€225
E-Learning
max 999
1 dag
AI+ Agent Specialty™
's-Hertogenbosch
ma 30 nov. 2026
Empower businesses with AI+ Agent Specialty™ to design, deploy, and scale intelligent agents.
Empower Automation with AI+ Agent Specialty™ for intelligent, efficient task execution
Beginner-Friendly Pathway: Perfect for learners stepping into the world of AI agents, offering simple, structured guidance for confident skill-building
Immersive Learning Experience: Combines essential AI agent fundamentals, intuitive tools, and real-world workflows to help you understand, build, and deploy automated agents
Action-Oriented Skill Development: Features practical exercises, scenario-based tasks, and guided projects so you can design, optimise, and showcase high-performance AI agents with ease
Module 1: Introduction to AI Agents
1.1 Understanding AI Agents
1.2 Anatomy and Ecosystem of AI Agents
1.3 Applications, Misconceptions, and Mini Case Studies
1.4 Case Study: Transforming Customer Support at Acme Retail with AI Agents
1.5 Hands-On Exercise 1: Build a Q&A ChatBot Using Gemini + Prompt + LLM Chain in Flowise Cloud
Module 2: Core Concepts & Types of AI Agents
2.1 Anatomy of an AI Agent
2.2 Classification of AI Agents
2.3 Matching Agents to Use Cases
2.4 Case Study: Enhancing Mental Health Support with AI Agents at Earkick
2.5 Hands-On Exercise
Module 3: Tools for Non-Coders
3.1 No-code and visual agent platforms
3.2 Tools Overview and Setup
3.3 Start building: “Your First Flow” with n8n
3.4 Case Study: Empowering HR with AI – Building an Onboarding Assistant Without Coding
3.5 Hands-on Exercise
Module 4: Building Simple Agents
4.1 Agent 1
4.2 Agent 2
4.3 Agent 3
4.4 Agent 4
4.5 Troubleshooting and Validation of AI Agents
4.6 Share Your AI Agent
4.7 Hands-On Exercise 1
Module 5: Multi-Tool Agents and Workflow Automation
5.1 Multi-Tool Agents
5.2 Agent Chaining and Workflow Basics
5.3 Managing Agent State: State, Context, and User Journey
5.4 Prompt Engineering for Agents
5.5 Multi-Agent Systems (MAS)
5.6 Case Study: Smarter Marketing Campaigns with Tool Chaining
5.7 Hands-on Exercise: Automating Order Tracking and Notifications with Make.com
Module 6: Integration, Application Mapping & Deployment
6.1 Deploying Agents
6.2 Channel Selection – Where the User will Interact
6.3 Hosting Environment – Where does the Agent Run?
6.4 Data Integration
6.5 Security Setup
6.6 Monitoring & Updates
6.7 Application Mapping
6.8 Hands-on Exercise 1: Integration of a Portfolio Assistant Chatbot into GitHub Pages using Zapier
Module 7: Monitoring, Guardrails & Responsible AI
7.1 Observability Basics
7.2 Performance Evaluation: Key Metrics
7.3 Guardrails: Preventing Misuse & Ensuring Safe Outputs
7.4 Responsible AI
7.5 Mini-Case: Failure and Recovery in Agent Deployments
7.6 Real-world Failures
7.7 Peer Sharing: How to Present and Discuss Agent Logs/Results
Module 8: Capstone Project – Design Your Own Intelligent Agent
8.1 Capstone Project 1: Smart Personal AI Assistant
8.2 Capstone Project 2: Smart Lead Engagement – From Email to Personalized Outreach – Sales Support Agent
8.3 Capstone Project 3: Education Tutor Agent
8.4 HR Knowledge Bot
8.5 Customer Service Agent
8.6 Healthcare Triage Bot
Tools you will explore
Python
LangChain
LlamaIndex
OpenAI API
Hugging Face Inference
Multi-Agent Orchestration Frameworks
Vector Databases (e.g., Pinecone, Chroma)
Workflow Orchestration (e.g., Airflow, Prefect)
Jupyter Notebooks
Docker
Prompt Engineering Platforms
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 + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Agent Specialty™ eLearning
Empower businesses with AI+ Agent Specialty™ to design, deploy, and scale intelligent agents.
Empower Automation with AI+ Agent Specialty™ for intelligent, efficient task execution
Beginner-Friendly Pathway: Perfect for learners stepping into the world of AI agents, offering simple, structured guidance for confident skill-building
Immersive Learning Experience: Combines essential AI agent fundamentals, intuitive tools, and real-world workflows to help you understand, build, and deploy automated agents
Action-Oriented Skill Development: Features practical exercises, scenario-based tasks, and guided projects so you can design, optimise, and showcase high-performance AI agents with ease
Module 1: Introduction to AI Agents
1.1 Understanding AI Agents
1.2 Anatomy and Ecosystem of AI Agents
1.3 Applications, Misconceptions, and Mini Case Studies
1.4 Case Study: Transforming Customer Support at Acme Retail with AI Agents
1.5 Hands-On Exercise 1: Build a Q&A ChatBot Using Gemini + Prompt + LLM Chain in Flowise Cloud
Module 2: Core Concepts & Types of AI Agents
2.1 Anatomy of an AI Agent
2.2 Classification of AI Agents
2.3 Matching Agents to Use Cases
2.4 Case Study: Enhancing Mental Health Support with AI Agents at Earkick
2.5 Hands-On Exercise
Module 3: Tools for Non-Coders
3.1 No-code and visual agent platforms
3.2 Tools Overview and Setup
3.3 Start building: “Your First Flow” with n8n
3.4 Case Study: Empowering HR with AI – Building an Onboarding Assistant Without Coding
3.5 Hands-on Exercise
Module 4: Building Simple Agents
4.1 Agent 1
4.2 Agent 2
4.3 Agent 3
4.4 Agent 4
4.5 Troubleshooting and Validation of AI Agents
4.6 Share Your AI Agent
4.7 Hands-On Exercise 1
Module 5: Multi-Tool Agents and Workflow Automation
5.1 Multi-Tool Agents
5.2 Agent Chaining and Workflow Basics
5.3 Managing Agent State: State, Context, and User Journey
5.4 Prompt Engineering for Agents
5.5 Multi-Agent Systems (MAS)
5.6 Case Study: Smarter Marketing Campaigns with Tool Chaining
5.7 Hands-on Exercise: Automating Order Tracking and Notifications with Make.com
Module 6: Integration, Application Mapping & Deployment
6.1 Deploying Agents
6.2 Channel Selection – Where the User will Interact
6.3 Hosting Environment – Where does the Agent Run?
6.4 Data Integration
6.5 Security Setup
6.6 Monitoring & Updates
6.7 Application Mapping
6.8 Hands-on Exercise 1: Integration of a Portfolio Assistant Chatbot into GitHub Pages using Zapier
Module 7: Monitoring, Guardrails & Responsible AI
7.1 Observability Basics
7.2 Performance Evaluation: Key Metrics
7.3 Guardrails: Preventing Misuse & Ensuring Safe Outputs
7.4 Responsible AI
7.5 Mini-Case: Failure and Recovery in Agent Deployments
7.6 Real-world Failures
7.7 Peer Sharing: How to Present and Discuss Agent Logs/Results
Module 8: Capstone Project – Design Your Own Intelligent Agent
8.1 Capstone Project 1: Smart Personal AI Assistant
8.2 Capstone Project 2: Smart Lead Engagement – From Email to Personalized Outreach – Sales Support Agent
8.3 Capstone Project 3: Education Tutor Agent
8.4 HR Knowledge Bot
8.5 Customer Service Agent
8.6 Healthcare Triage Bot
Tools you will explore
Python
LangChain
LlamaIndex
OpenAI API
Hugging Face Inference
Multi-Agent Orchestration Frameworks
Vector Databases (e.g., Pinecone, Chroma)
Workflow Orchestration (e.g., Airflow, Prefect)
Jupyter Notebooks
Docker
Prompt Engineering Platforms
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 + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Pharma Practitioner™
Nieuwegein
wo 16 sep. 2026
en 1 andere data
Formerly known as AI+ Pharma™
Harness AI in Pharma to speed drug discovery, optimize trials, and enable precision therapies.
Revolutionize Healthcare Expertise with AI+ Pharma Practitioner™ for Smarter, Data-Driven Decisions
Beginner-Friendly Pathway: Ideal for learners and professionals entering the world of AI in pharmaceuticals, offering clear fundamentals and easy-to-grasp concepts
Integrated Learning Experience: Combines core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to strengthen analytical and operational skills
Industry-Focused Growth: Equips you with practical projects, scenario-based exercises, and actionable insights to help you apply AI in drug development, research, compliance, and patient-centric solutions
Module 1: AI Foundations for Pharma
1.1 AI and Machine Learning Basics
1.2 AI Algorithms and Models
1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)
Module 2: AI in Drug Discovery and Development
2.1 AI in Molecular Drug Design
2.2 AI in Drug Repurposing
2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB
Module 3: Clinical Trials Optimization with AI
3.1 AI-Enhanced Patient Recruitment
3.2 Clinical Data Management and Monitoring
3.3 Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)
Module 4: Precision Medicine and Genomics
4.1 Personalized Treatment Strategies
4.2 Biomarker Discovery
4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
4.4 Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal
Module 5: Regulatory and Ethical AI in Pharma
5.1 Ethical Considerations and AI Governance
5.2 AI Compliance and Regulatory Frameworks
5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
5.5 Hands-on: Literature Mining with LitVar 2.0
Module 6: Implementing AI in Pharma Projects
6.1 AI Project Management
6.2 Evaluating AI Tools and ROI
6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management
Module 7: Future Trends and Sustainability in Pharma AI
7.1 Emerging AI Technologies in Pharma
7.2 AI for Sustainable Healthcare
7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders
7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making
Module 8: Capstone Project
8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy
8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention
8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases
8.4 Capstone Project Evaluation Scheme
Tools you will explore
Python
TensorFlow
PyTorch
Scikit-learn
Pandas
NumPy
SQL
Jupyter Notebooks
MLflow
DataBricks
RDKit
DeepChem
Biopython
Hugging Face Transformers for Biomedical NLP
spaCy / Clinical NLP Toolkits
Apache Spark for Healthcare Data
Power BI / Tableau for Clinical Dashboards
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+ Pharma Practitioner™ eLearning
Formerly known as AI+ Pharma™
Harness AI in Pharma to speed drug discovery, optimize trials, and enable precision therapies.
Revolutionize Healthcare Expertise with AI+ Pharma Practitioner™ for Smarter, Data-Driven Decisions
Beginner-Friendly Pathway: Ideal for learners and professionals entering the world of AI in pharmaceuticals, offering clear fundamentals and easy-to-grasp concepts
Integrated Learning Experience: Combines core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to strengthen analytical and operational skills
Industry-Focused Growth: Equips you with practical projects, scenario-based exercises, and actionable insights to help you apply AI in drug development, research, compliance, and patient-centric solutions
Module 1: AI Foundations for Pharma
1.1 AI and Machine Learning Basics
1.2 AI Algorithms and Models
1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)
Module 2: AI in Drug Discovery and Development
2.1 AI in Molecular Drug Design
2.2 AI in Drug Repurposing
2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB
Module 3: Clinical Trials Optimization with AI
3.1 AI-Enhanced Patient Recruitment
3.2 Clinical Data Management and Monitoring
3.3 Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)
Module 4: Precision Medicine and Genomics
4.1 Personalized Treatment Strategies
4.2 Biomarker Discovery
4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
4.4 Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal
Module 5: Regulatory and Ethical AI in Pharma
5.1 Ethical Considerations and AI Governance
5.2 AI Compliance and Regulatory Frameworks
5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
5.5 Hands-on: Literature Mining with LitVar 2.0
Module 6: Implementing AI in Pharma Projects
6.1 AI Project Management
6.2 Evaluating AI Tools and ROI
6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management
Module 7: Future Trends and Sustainability in Pharma AI
7.1 Emerging AI Technologies in Pharma
7.2 AI for Sustainable Healthcare
7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders
7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making
Module 8: Capstone Project
8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy
8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention
8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases
8.4 Capstone Project Evaluation Scheme
Tools you will explore
Python
TensorFlow
PyTorch
Scikit-learn
Pandas
NumPy
SQL
Jupyter Notebooks
MLflow
DataBricks
RDKit
DeepChem
Biopython
Hugging Face Transformers for Biomedical NLP
spaCy / Clinical NLP Toolkits
Apache Spark for Healthcare Data
Power BI / Tableau for Clinical Dashboards
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+ Mining Practitioner™
Formerly known as AI+ Mining™
Unlock the potential of AI in Mining to optimize exploration, improve resource management, and automate operations.
Powering the Next Era of Mining with AI: Smarter, Safer, and Sustainable Operations
Beginner-Friendly Course: Perfect introduction to explore how AI transforms modern mining practices
Foundational Learning: Explains AI-driven exploration, automation, data analysis, and safety innovations
No Technical Background Needed: Open to anyone eager to understand the role of technology in mining
Module 1: Introduction to AI in Mining
1.1 Overview of AI, ML & Deep Learning in Mining
1.2 Use Cases
1.3 Activity
Module 2: Machine Learning & Deep Learning for Mining
2.1 Introduction to ML & Deep Learning
2.2 Use Cases
2.3 Case Study
2.4 Hands-On Exercise
2.5 Activity
Module 3: AI in Mineral Exploration & Resource Modeling
3.1 AI for Smart Exploration & Orebody Modeling
3.2 Use-Cases
3.3 Case Study
3.4 Hands-on Exercises
3.5 Activity
Module 4: AI for Equipment Automation & Fleet Optimization
4.1 AI in Autonomous Vehicles & Robotics
4.2 Use Cases
4.3 Case Study
4.4 Hands-On Exercise
4.5 Activity
Module 5: AI in Predictive Maintenance & Asset Management
5.1 AI in Equipment Health Monitoring
5.2 Use Cases
5.3 Case Study
5.4 Hands-On Exercise
5.5 Activity
Module 6: AI for Environmental Compliance & Sustainability
6.1 AI-Powered Environmental Monitoring
6.2 Use Cases
6.3 Case Study
6.4 Hands-On Exercises
6.5 Activity: Group Exercise
Module 7: AI for Workforce Transformation & Ethical AI
7.1 Ethical AI, Workforce Augmentation & AI Regulations
7.2 Use Cases
7.3 Case Study
7.4 Hands-On Exercises
Module 8: AI in Mining Strategy & Implementation
8.1 AI-Driven Decision-Making in Mining
8.2 Use Cases
8.3 Case Study
Tools you will explore
TensorFlow
Keras
Hadoop
Python
Tableau
Matplotlib
SQL
Apache Spark
Predictive Maintenance Software
Mining Simulation Tools
Computer Vision Tools
IoT Integration Platforms
Accela Civic Platform
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+ Mining Practitioner™ eLearning
Formerly known as AI+ Mining™
Unlock the potential of AI in Mining to optimize exploration, improve resource management, and automate operations.
Powering the Next Era of Mining with AI: Smarter, Safer, and Sustainable Operations
Beginner-Friendly Course: Perfect introduction to explore how AI transforms modern mining practices
Foundational Learning: Explains AI-driven exploration, automation, data analysis, and safety innovations
No Technical Background Needed: Open to anyone eager to understand the role of technology in mining
Module 1: Introduction to AI in Mining
1.1 Overview of AI, ML & Deep Learning in Mining
1.2 Use Cases
1.3 Activity
Module 2: Machine Learning & Deep Learning for Mining
2.1 Introduction to ML & Deep Learning
2.2 Use Cases
2.3 Case Study
2.4 Hands-On Exercise
2.5 Activity
Module 3: AI in Mineral Exploration & Resource Modeling
3.1 AI for Smart Exploration & Orebody Modeling
3.2 Use-Cases
3.3 Case Study
3.4 Hands-on Exercises
3.5 Activity
Module 4: AI for Equipment Automation & Fleet Optimization
4.1 AI in Autonomous Vehicles & Robotics
4.2 Use Cases
4.3 Case Study
4.4 Hands-On Exercise
4.5 Activity
Module 5: AI in Predictive Maintenance & Asset Management
5.1 AI in Equipment Health Monitoring
5.2 Use Cases
5.3 Case Study
5.4 Hands-On Exercise
5.5 Activity
Module 6: AI for Environmental Compliance & Sustainability
6.1 AI-Powered Environmental Monitoring
6.2 Use Cases
6.3 Case Study
6.4 Hands-On Exercises
6.5 Activity: Group Exercise
Module 7: AI for Workforce Transformation & Ethical AI
7.1 Ethical AI, Workforce Augmentation & AI Regulations
7.2 Use Cases
7.3 Case Study
7.4 Hands-On Exercises
Module 8: AI in Mining Strategy & Implementation
8.1 AI-Driven Decision-Making in Mining
8.2 Use Cases
8.3 Case Study
Tools you will explore
TensorFlow
Keras
Hadoop
Python
Tableau
Matplotlib
SQL
Apache Spark
Predictive Maintenance Software
Mining Simulation Tools
Computer Vision Tools
IoT Integration Platforms
Accela Civic Platform
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+ Customer Service Practitioner™
Formerly known as AI+ Customer Service™
Enhance Customer Experiences: Employ AI-Powered Service Solutions
Customer-Centric AI: Redefine service workflows with AI-powered personalization
Practical Execution: Implement automation tools to optimize CX and satisfaction
Ethical AI Integration: Covers trust-building and responsible AI practices
Competitive Edge: Learn to enhance communication and service delivery at scale
Course Overview
Course Introduction Preview
Module 1: Introduction to Artificial Intelligence (AI) in Customer Service
1.1 Overview of AI
1.2 Relevance of AI in Customer Service
Module 2: Understanding AI Technologies
2.1 Overview of Machine Learning
2.2 Natural Language Processing (NLP)
2.3 Deep Learning and Neural Networks
2.4 AI-Driven Analytics
Module 3: Data Collection and Analysis
3.1 Gathering Customer Data
3.2 Data Quality and Integrity
3.3 Analyzing Data for Insights
3.4 Applying Insights to Enhance Customer Service
Module 4: Implementing AI Solutions
4.1 AI Solutions for Customer Service
4.2 Integration into Customer Service Systems
4.3 Training and Change Management
4.4 Measuring the Impact of AI on Customer Service
Module 5: Optimizing Customer Experiences
5.1 Using AI to Create Personalized Customer Interactions
5.2 Increasing Service Efficiency with AI
5.3 Case Studies: Successful AI Implementations in Customer Service
Module 6: Ethical Considerations and Trust
6.1 Ethical AI Use in Customer Service
6.2 Building Trust through Transparency
6.3 Compliance with Data Privacy Regulations
Module 7: Future of AI in Customer Service
7.1 Emerging Trends and Advancements in AI Technologies
7.2 Innovative Use Cases for AI in Customer Service
7.3 Preparing for AI Evolution in Customer Service
7.4 Ethical and Societal Considerations
Module 8: Creating an AI Strategy for Your Organization
8.1 Developing Strategic Plan for AI Implementation and Evolution
8.2 Cultivating an AI-Driven Culture
8.3 Overcoming Challenges and Measuring Success
Optional Module: AI Agents for Customer Service
1. What Are AI Agents
2. Types of AI Agents
3. Applications and Trends of AI Agents in Customer Service
Tools you will explore
Zendesk
Freddy AI
Octane AI
Rul.ai
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+ Customer Service Practitioner™ eLearning
Formerly known as AI+ Customer Service™
Enhance Customer Experiences: Employ AI-Powered Service Solutions
Customer-Centric AI: Redefine service workflows with AI-powered personalization
Practical Execution: Implement automation tools to optimize CX and satisfaction
Ethical AI Integration: Covers trust-building and responsible AI practices
Competitive Edge: Learn to enhance communication and service delivery at scale
Course Overview
Course Introduction Preview
Module 1: Introduction to Artificial Intelligence (AI) in Customer Service
1.1 Overview of AI
1.2 Relevance of AI in Customer Service
Module 2: Understanding AI Technologies
2.1 Overview of Machine Learning
2.2 Natural Language Processing (NLP)
2.3 Deep Learning and Neural Networks
2.4 AI-Driven Analytics
Module 3: Data Collection and Analysis
3.1 Gathering Customer Data
3.2 Data Quality and Integrity
3.3 Analyzing Data for Insights
3.4 Applying Insights to Enhance Customer Service
Module 4: Implementing AI Solutions
4.1 AI Solutions for Customer Service
4.2 Integration into Customer Service Systems
4.3 Training and Change Management
4.4 Measuring the Impact of AI on Customer Service
Module 5: Optimizing Customer Experiences
5.1 Using AI to Create Personalized Customer Interactions
5.2 Increasing Service Efficiency with AI
5.3 Case Studies: Successful AI Implementations in Customer Service
Module 6: Ethical Considerations and Trust
6.1 Ethical AI Use in Customer Service
6.2 Building Trust through Transparency
6.3 Compliance with Data Privacy Regulations
Module 7: Future of AI in Customer Service
7.1 Emerging Trends and Advancements in AI Technologies
7.2 Innovative Use Cases for AI in Customer Service
7.3 Preparing for AI Evolution in Customer Service
7.4 Ethical and Societal Considerations
Module 8: Creating an AI Strategy for Your Organization
8.1 Developing Strategic Plan for AI Implementation and Evolution
8.2 Cultivating an AI-Driven Culture
8.3 Overcoming Challenges and Measuring Success
Optional Module: AI Agents for Customer Service
1. What Are AI Agents
2. Types of AI Agents
3. Applications and Trends of AI Agents in Customer Service
Tools you will explore
Zendesk
Freddy AI
Octane AI
Rul.ai
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+ Finance Practitioner™
Formerly known as AI+ Finance™
Maximize Returns with AI-Enhanced Financial Strategies
Finance Transformation: Explore AI use in credit risk, fraud detection, and forecasting
Smart Modelling: Apply predictive analytics and blockchain in financial strategies
Practical AI Tools: Optimize operations and decision-making with hands-on training
Strategic Readiness: Build financial resilience in complex economic ecosystems
Course Overview
Course Introduction Preview
Module 1: Introduction to Artificial Intelligence (AI) and Its Impact on Finance
1.1 Fundamentals of AI in Finance
1.2 Data-Driven Decision Making in Finance
1.3 AI Technologies Shaping the Financial Landscape
Module 2: Data-Driven Decision Making in Finance
2.1 The Power of Financial Data
2.2 Analytics and Insights in Finance
2.3 Implementing AI for Strategic Financial Decision-Making
Module 3: Enhancing Credit and Loans with AI
3.1 Revolutionizing Credit Scoring with AI
3.2 Automating Loan Origination and Processing
3.3 Personalization and Customer Experience in Lending
Module 4: Fraud Detection in Finance with AI
4.1 The Landscape of Financial Fraud
4.2 AI and Machine Learning in Fraud Detection
4.3 Future Directions in AI-driven Fraud Detection
Module 5: Forecasting Stock Market with AI
5.1 Overview of Stock Market Analysis
5.2 AI Technologies in Stock Forecasting
5.3 Challenges and Future of AI in Stock Market Forecasting
Module 6: Blockchain and AI: Revolutionizing Finance
6.1 Introduction to Blockchain in Finance
6.2 Synergy of AI and Blockchain in Finance
6.3 Future Perspectives and Ethical Considerations
Module 7: Emerging Technologies and Their Impact on Finance
7.1 The Expanding Universe of FinTech
7.2 Next-Generation Technologies Shaping Finance
7.3 Integrating Emerging Technologies into Financial Services
Module 8: Implementing AI Strategies in Finance
8.1 Building a Digital-First Finance Strategy
8.2 Operationalizing AI and Emerging Technologies
8.3 Looking Ahead: The Future of Financial Services
Optional Module: AI Agents for Finance
1. What Are AI Agents for Finance
2. Types of AI Agents in Finance
3. Significance of AI Agents in Finance
Tools you will explore
Sentieo
Magnifi
QuantConnect
AlphaSense
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