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AI+ Customer Service Practitioner™ eLearning
Formerly known as AI+ Customer Service™ <br> <br> 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™ <br> <br> 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
AI+ Finance Practitioner™ eLearning
Formerly known as AI+ Finance™ <br> <br> 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
€225
E-Learning
max 999
1 dag
AI+ Finance Agent Specialty™
Formerly known as AI+ Finance Agent™ <br> <br> Empower organizations with AI+ Finance Agent Specialty™ to automate financial operations and improve decisions
Core Concepts Covered: Learn AI fundamentals for finance, focusing on analytics, trading, risk, fraud, automation
Capstone Application: Build practical AI finance agents supporting trading, risk evaluation, fraud monitoring, and forecasting
Career Readiness: Gain expertise in AI-powered financial roles through mentorship, hands-on training, designing AI agents for finance innovation
Module 1: Introduction to AI Agents in Finance
1.1 Understanding AI Agents in Finance vs Traditional Financial Automation
1.2 The Evolution of AI Agents in Financial Services
1.3 Overview of Different Types of AI Agents in Finance
1.4 Importance of Agent Autonomy and Task Delegation in Financial Settings
1.5 Key Differences Between AI Agents in Finance and Traditional Automation
1.6 Hands-On Activity: Exploring AI Agents in Finance
Module 2: Building and Understanding AI Agents in Finance
2.1 Architecture of AI Agents in Finance
2.2 Tools and Libraries for Agent Development
2.3 AI Agents vs. Static Models
2.4 Overview of Agent Lifecycle
2.5 Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes
2.6 Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI
2.7 Hands-On Activity: Building and Understanding AI Agents in Finance
Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring
3.1 Supervised/Unsupervised ML for Fraud Detection
3.2 Pattern Analysis & Behavioural Profiling
3.3 Real-time Monitoring Agents
3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
3.5 Case Study: PayPal’s AI System Uses Graph-Based Anomaly Detection Agents to Flag 0.32% of All Transactions for Fraud with 99.9% Accuracy
3.6 Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring
Module 4: AI Agents for Credit Scoring and Lending Automation
4.1 Feature Generation from Non-Traditional Credit Data
4.2 Explainability (XAI) in Credit Decisions
4.3 Bias Mitigation in Lending Agents
4.4 Real-World Use Case: Agents Assessing New-to-Credit Individuals Using Transaction and Mobile Data
4.5 Case Study: Upstart’s AI-Based Lending Platform Approved by CFPB Showed 27% Increase in Approval Rate and 16% Lower APRs for Borrowers
4.6 Hands-On Activity: AI Agents for Credit Scoring and Lending Automation
Module 5: AI Agents for Wealth Management and Robo-Advisory
5.1 Personalization Using Profiling Agents
5.2 Portfolio Rebalancing Algorithms
5.3 Sentiment-Aware Investing
5.4 Real-World Use Case: AI Agent Adjusting Portfolio Weekly Based on Financial Goals and Market Trends
5.5 Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths
5.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory
Module 6: Trading Bots and Market-Monitoring Agents
6.1 Reinforcement Learning in Trading Agents
6.2 Predictive Modelling Using Historical Data
6.3 Risk-Reward Threshold Management
6.4 Real-World Use Case: AI Trading Agents Performing Arbitrage Between Crypto Exchanges
6.4 Case Study: Renaissance Technologies Utilizes AI to Automate Short-Hold Trades, Generating Consistent Alpha via Adaptive Trading Bots
6.5 Hands-On Activity: Trading Bots and Market-Monitoring Agents
Module 7: NLP Agents for Financial Document Intelligence
7.1 LLMs in Earnings Call and Filings Analysis
7.2 AI Summarization and Event Detection
7.3 Voice-to-Text and Key-Point Extraction
7.4 Real-World Use Case
7.5 Case Study: BloombergGPT — A Financial-Grade Large Language Model
7.6 Hands-On Activity: NLP Agents for Financial Document Intelligence
Module 8: Compliance and Risk Surveillance Agents
8.1 AI for Anti-Money Laundering (AML) and Know Your Business (KYB)
8.2 Regulation-aware Rule Modelling
8.3 Transaction Graph Analysis
8.4 Real-World Use Case: Agent tracking suspicious cross-border money transfers in real-time across multiple accounts.
8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity detection by 30%.
8.6 Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems
Module 9: Responsible, Fair & Auditable AI Agents
9.1 Governance Frameworks for AI in Finance (RBI, EU AI Act)
9.2 Transparency and Auditability in Decision Logic
9.3 Fairness and Explainability
9.4 Real-World Use Case: Auditable AI Agent Logs Used During Internal Policy Audits to Ensure Fair Lending practices.
9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory scrutiny.
9.6 Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance
Module 10: World Famous Case Studies
10.1 Case Study 1: JPMorgan’s COiN Platform
10.2 Case Study 2: AI in Fraud Detection – PayPal’s Decision Intelligence
10.3 Case Study: AI-Driven Credit Scoring – Upstart’s Lending Platform
10.4 Capstone Project
10.5 Key Takeaways of the Module
Tools you will explore
Python
TensorFlow
Pandas
NumPy
Power BI
SQL
OpenAI API
APIs
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+ Finance Agent Specialty™ eLearning
Formerly known as AI+ Finance Agent™ <br> <br> Empower organizations with AI+ Finance Agent Specialty™ to automate financial operations and improve decisions
Core Concepts Covered: Learn AI fundamentals for finance, focusing on analytics, trading, risk, fraud, automation
Capstone Application: Build practical AI finance agents supporting trading, risk evaluation, fraud monitoring, and forecasting
Career Readiness: Gain expertise in AI-powered financial roles through mentorship, hands-on training, designing AI agents for finance innovation
Module 1: Introduction to AI Agents in Finance
1.1 Understanding AI Agents in Finance vs Traditional Financial Automation
1.2 The Evolution of AI Agents in Financial Services
1.3 Overview of Different Types of AI Agents in Finance
1.4 Importance of Agent Autonomy and Task Delegation in Financial Settings
1.5 Key Differences Between AI Agents in Finance and Traditional Automation
1.6 Hands-On Activity: Exploring AI Agents in Finance
Module 2: Building and Understanding AI Agents in Finance
2.1 Architecture of AI Agents in Finance
2.2 Tools and Libraries for Agent Development
2.3 AI Agents vs. Static Models
2.4 Overview of Agent Lifecycle
2.5 Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes
2.6 Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI
2.7 Hands-On Activity: Building and Understanding AI Agents in Finance
Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring
3.1 Supervised/Unsupervised ML for Fraud Detection
3.2 Pattern Analysis & Behavioural Profiling
3.3 Real-time Monitoring Agents
3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
3.5 Case Study: PayPal’s AI System Uses Graph-Based Anomaly Detection Agents to Flag 0.32% of All Transactions for Fraud with 99.9% Accuracy
3.6 Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring
Module 4: AI Agents for Credit Scoring and Lending Automation
4.1 Feature Generation from Non-Traditional Credit Data
4.2 Explainability (XAI) in Credit Decisions
4.3 Bias Mitigation in Lending Agents
4.4 Real-World Use Case: Agents Assessing New-to-Credit Individuals Using Transaction and Mobile Data
4.5 Case Study: Upstart’s AI-Based Lending Platform Approved by CFPB Showed 27% Increase in Approval Rate and 16% Lower APRs for Borrowers
4.6 Hands-On Activity: AI Agents for Credit Scoring and Lending Automation
Module 5: AI Agents for Wealth Management and Robo-Advisory
5.1 Personalization Using Profiling Agents
5.2 Portfolio Rebalancing Algorithms
5.3 Sentiment-Aware Investing
5.4 Real-World Use Case: AI Agent Adjusting Portfolio Weekly Based on Financial Goals and Market Trends
5.5 Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths
5.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory
Module 6: Trading Bots and Market-Monitoring Agents
6.1 Reinforcement Learning in Trading Agents
6.2 Predictive Modelling Using Historical Data
6.3 Risk-Reward Threshold Management
6.4 Real-World Use Case: AI Trading Agents Performing Arbitrage Between Crypto Exchanges
6.4 Case Study: Renaissance Technologies Utilizes AI to Automate Short-Hold Trades, Generating Consistent Alpha via Adaptive Trading Bots
6.5 Hands-On Activity: Trading Bots and Market-Monitoring Agents
Module 7: NLP Agents for Financial Document Intelligence
7.1 LLMs in Earnings Call and Filings Analysis
7.2 AI Summarization and Event Detection
7.3 Voice-to-Text and Key-Point Extraction
7.4 Real-World Use Case
7.5 Case Study: BloombergGPT — A Financial-Grade Large Language Model
7.6 Hands-On Activity: NLP Agents for Financial Document Intelligence
Module 8: Compliance and Risk Surveillance Agents
8.1 AI for Anti-Money Laundering (AML) and Know Your Business (KYB)
8.2 Regulation-aware Rule Modelling
8.3 Transaction Graph Analysis
8.4 Real-World Use Case: Agent tracking suspicious cross-border money transfers in real-time across multiple accounts.
8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity detection by 30%.
8.6 Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems
Module 9: Responsible, Fair & Auditable AI Agents
9.1 Governance Frameworks for AI in Finance (RBI, EU AI Act)
9.2 Transparency and Auditability in Decision Logic
9.3 Fairness and Explainability
9.4 Real-World Use Case: Auditable AI Agent Logs Used During Internal Policy Audits to Ensure Fair Lending practices.
9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory scrutiny.
9.6 Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance
Module 10: World Famous Case Studies
10.1 Case Study 1: JPMorgan’s COiN Platform
10.2 Case Study 2: AI in Fraud Detection – PayPal’s Decision Intelligence
10.3 Case Study: AI-Driven Credit Scoring – Upstart’s Lending Platform
10.4 Capstone Project
10.5 Key Takeaways of the Module
Tools you will explore
Python
TensorFlow
Pandas
NumPy
Power BI
SQL
OpenAI API
APIs
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+ Human Resources Practitioner™
Formerly known as AI+ Human Resources™
Building Tomorrow's Workforce Today with AI-Driven Solutions
AI in Workforce: Learn to integrate AI for smarter recruitment and performance systems
Data-Driven HR: Gain insights into talent acquisition and evaluation using ML
Ethical Application: Understand responsible AI practices in people management
Future-Ready Skills: Prepare to handle evolving HR dynamics with efficiency and equity
Certification Overview
Course Introduction Preview
Module 1: Foundations of Artificial Intelligence (AI) in HR
1.1 Introduction to AI Technologies
1.2 AI’s Role in HR Evolution
1.3 AI Applications in HR
1.4 Preparing HR for AI Integration
Module 2: AI-Enhanced Recruitment and Onboarding
2.1 Revolutionizing Recruitment with AI
2.2 Enhancing Onboarding with AI
2.3 Implementing AI in Recruitment and Onboarding
Module 3: Enhancing Employee Experience and Engagement
3.1 Personalizing Employee Development with AI
3.2 AI for Employee Engagement and Sentiment Analysis
3.3 Implementing AI Solutions for Employee Experience
Module 4: Workforce Analytics and Talent Management
4.1 Introduction to Workforce Analytics
4.2 Predictive Analytics for HR
4.3 AI in Talent Management and Succession Planning
4.4 Ethical Considerations in Workforce Analytics
Module 5: Ethical AI and Bias Mitigation
5.1 Understanding Ethical AI in HR
5.2 Identifying and Mitigating Bias in AI Tools
5.3 Implementing Ethical AI Practices in HR
5.4 Building an Ethical AI Culture
Module 6: Legal Considerations in AI for HR
6.1 Legal Landscape for AI in HR
6.2 Compliance Strategies for AI in HR
6.3 Navigating Regulatory Changes
6.4 Ethical and Legal Alignment
Module 7: Preparing for the Future of AI in HR
7.1 Future Trends in AI and HR
7.2 Building Organizational Readiness for AI
7.3 Strategic Planning for AI Adoption
7.4 Ethical and Future Considerations
Module 8: Implementing AI in HR: A Practical Workshop
8.1 Project Planning and Design
8.2 Implementation Strategy
8.3 Monitoring, Evaluation, and Scaling
8.4 Ethical and Legal Considerations
Optional Module: AI Agents for Human Resources
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
TensorFlow
Scikit-learn
AI Fairness 360
Zotero
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+ Human Resources Practitioner™ eLearning
Formerly known as AI+ Human Resources™
Building Tomorrow's Workforce Today with AI-Driven Solutions
AI in Workforce: Learn to integrate AI for smarter recruitment and performance systems
Data-Driven HR: Gain insights into talent acquisition and evaluation using ML
Ethical Application: Understand responsible AI practices in people management
Future-Ready Skills: Prepare to handle evolving HR dynamics with efficiency and equity
Certification Overview
Course Introduction Preview
Module 1: Foundations of Artificial Intelligence (AI) in HR
1.1 Introduction to AI Technologies
1.2 AI’s Role in HR Evolution
1.3 AI Applications in HR
1.4 Preparing HR for AI Integration
Module 2: AI-Enhanced Recruitment and Onboarding
2.1 Revolutionizing Recruitment with AI
2.2 Enhancing Onboarding with AI
2.3 Implementing AI in Recruitment and Onboarding
Module 3: Enhancing Employee Experience and Engagement
3.1 Personalizing Employee Development with AI
3.2 AI for Employee Engagement and Sentiment Analysis
3.3 Implementing AI Solutions for Employee Experience
Module 4: Workforce Analytics and Talent Management
4.1 Introduction to Workforce Analytics
4.2 Predictive Analytics for HR
4.3 AI in Talent Management and Succession Planning
4.4 Ethical Considerations in Workforce Analytics
Module 5: Ethical AI and Bias Mitigation
5.1 Understanding Ethical AI in HR
5.2 Identifying and Mitigating Bias in AI Tools
5.3 Implementing Ethical AI Practices in HR
5.4 Building an Ethical AI Culture
Module 6: Legal Considerations in AI for HR
6.1 Legal Landscape for AI in HR
6.2 Compliance Strategies for AI in HR
6.3 Navigating Regulatory Changes
6.4 Ethical and Legal Alignment
Module 7: Preparing for the Future of AI in HR
7.1 Future Trends in AI and HR
7.2 Building Organizational Readiness for AI
7.3 Strategic Planning for AI Adoption
7.4 Ethical and Future Considerations
Module 8: Implementing AI in HR: A Practical Workshop
8.1 Project Planning and Design
8.2 Implementation Strategy
8.3 Monitoring, Evaluation, and Scaling
8.4 Ethical and Legal Considerations
Optional Module: AI Agents for Human Resources
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
TensorFlow
Scikit-learn
AI Fairness 360
Zotero
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+ Marketing Practitioner™
Formerly known as AI+ Marketing™ <br> <br> Unlock Marketing Potential: Employ Advanced AI Technologies
AI-Powered Marketing: Explore predictive analytics, customer journey mapping, and automation
Strategic Impact: Learn to develop data-backed, personalized marketing strategies
Future-Focused Tools: Includes chatbots, AI content, and trend forecasting
ROI & Retention: Boost lead generation, customer retention, and innovation
Course Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) in Marketing
1.1 Understanding AI and Its Core Components Preview
1.2 Historical Context and Evolution of AI in Marketing Preview
1.3 AI Technologies Transforming Marketing Preview
1.4 Applications of AI in Marketing Module 2: AI-Driven Content Strategy and Personalization
2.1 Introduction to AI in Content Marketing Preview
2.2 Personalization Through AI Preview
2.3 Implementing AI in Your Content Strategy Module 3: AI in Social Media and Email Marketing
3.1 AI Integration in Social Media Marketing Preview
3.2 Leveraging AI for Email Marketing Success Module 4: Leveraging AI for Marketing Analytics
4.1 Introduction to AI-powered Analytics
4.2 Predictive Analytics and Consumer Behavior
4.3 Measuring and Optimizing Campaign Effectiveness
Module 5: Ethical Considerations in AI Marketing
5.1 Ethical AI Use in Marketing
5.2 Regulatory Compliance and Standards
5.3 Implementing Ethical AI Marketing Practices
Module 6: Crafting an AI-Driven Marketing Strategy
6.1 Strategic Planning with AI
6.2 Implementation of AI in Marketing Strategies
6.3 Creating a Scalable AI Marketing Plan
Module 7: AI Integration in Multichannel Marketing Campaigns
7.1 Integrating AI in Multichannel Strategies
7.2 Measuring the Effectiveness of AI-Enhanced Campaigns
7.3 Future Trends in AI and Multichannel Marketing
Optional Module: AI Agents for Marketing
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
HubSpot
Copy.ai
ActiveCampaign
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+ Marketing Practitioner™ eLearning
Formerly known as AI+ Marketing™ <br> <br> Unlock Marketing Potential: Employ Advanced AI Technologies
AI-Powered Marketing: Explore predictive analytics, customer journey mapping, and automation
Strategic Impact: Learn to develop data-backed, personalized marketing strategies
Future-Focused Tools: Includes chatbots, AI content, and trend forecasting
ROI & Retention: Boost lead generation, customer retention, and innovation
Course Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) in Marketing
1.1 Understanding AI and Its Core Components Preview
1.2 Historical Context and Evolution of AI in Marketing Preview
1.3 AI Technologies Transforming Marketing Preview
1.4 Applications of AI in Marketing Module 2: AI-Driven Content Strategy and Personalization
2.1 Introduction to AI in Content Marketing Preview
2.2 Personalization Through AI Preview
2.3 Implementing AI in Your Content Strategy Module 3: AI in Social Media and Email Marketing
3.1 AI Integration in Social Media Marketing Preview
3.2 Leveraging AI for Email Marketing Success Module 4: Leveraging AI for Marketing Analytics
4.1 Introduction to AI-powered Analytics
4.2 Predictive Analytics and Consumer Behavior
4.3 Measuring and Optimizing Campaign Effectiveness
Module 5: Ethical Considerations in AI Marketing
5.1 Ethical AI Use in Marketing
5.2 Regulatory Compliance and Standards
5.3 Implementing Ethical AI Marketing Practices
Module 6: Crafting an AI-Driven Marketing Strategy
6.1 Strategic Planning with AI
6.2 Implementation of AI in Marketing Strategies
6.3 Creating a Scalable AI Marketing Plan
Module 7: AI Integration in Multichannel Marketing Campaigns
7.1 Integrating AI in Multichannel Strategies
7.2 Measuring the Effectiveness of AI-Enhanced Campaigns
7.3 Future Trends in AI and Multichannel Marketing
Optional Module: AI Agents for Marketing
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
HubSpot
Copy.ai
ActiveCampaign
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+ Product Manager Fundamentals™
Formerly known as AI+ Product Manager™ <br> <br> Innovate Products Faster with AI-Enabled Management
Product Innovation: Leverage AI to drive product development and market fit
Concept to Execution: Explore AI applications in real product lifecycle scenarios
Market Advantage: Bridge the gap between tech innovation and customer needs
Leadership Ready: Prepare for leading roles in fast-evolving product ecosystems
Certification Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) for Product Managers
1.1 Understanding the Basics of artificial intelligence
1.2 Importance of AI
Module 2: Fundamentals of Machine Learning
2.1 Introduction to Machine Learning
2.2 Data Preparation in ML model
Module 3: AI Product Development Lifecycle
3.1 Exploring How AI Can Be Leveraged in Ideation and Conceptualization
3.2 Prototyping and Testing: Explore Methods for Prototyping and Testing AI-driven Products Effectively
Module 4: AI Ethics and Bias
4.1 Understanding Ethical Considerations: Examine the Ethical Implications of AI Products and the Responsibility of Product Managers
4.2 Mitigating Bias: Learn Strategies to Identify and Address Bias in AI Algorithms and Products
Module 5: AI Implementation Strategies
5.1 Integration with Existing Products: Explore Methods for Integrating AI Features into Existing Products Seamlessly
5.2 Stakeholder Management: Understand How to Communicate AI Initiatives Effectively with Stakeholders and Gain their Support
Module 6: AI Metrics and Performance Evaluation
6.1 Key Performance Indicators (KPIs): Identify Relevant Metrics for Measuring the Success of AI-driven Products
6.2 Performance Evaluation Techniques: Learn Methods for Evaluating the Performance of AI Models and Products
Module 7: AI Regulation and Compliance
7.1 Regulatory Landscape: Explore Current Regulations and Frameworks Relevant to AI Products
7.2 Compliance Strategies: Develop Strategies to Ensure AI Products Comply with Regulatory Requirements
Module 8: Future Trends in AI and Product Management
8.1 Emerging Technologies: Discuss Upcoming Trends and Technologies Shaping the Future of AI and Product Management
8.2 Strategic Planning: Learn How to Anticipate and Adapt to Future Changes in the AI Landscape to Drive Product Innovation
Optional Module: AI Agents for Product Management
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
ChatGPT
AI Fairness 360
Power BI
IBM Watson OpenScale
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