Opleidingen
68.894
resultaten
AI+ Quantum Practitioner™
Formerly known as AI+ Quantum™ <br> <br>Harness Quantum Power with AI
AI + Quantum Integration: Explore Quantum Gates, Circuits, and AI applications
Advanced Learnings: Includes Quantum Deep Learning and transformative AI methodologies
Industry-Oriented: Real-world case studies and trend analysis
Ethical Focus: Learn implications of quantum AI responsibly and efficiently
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
1.1 Artificial Intelligence Refresher
1.2 Quantum Computing Refresher
Module 2: Quantum Computing Gates, Circuits, and Algorithms
2.1 Quantum Gates and their Representation
2.2 Multi Qubit Systems and Multi Qubit Gates
Module 3: Quantum Algorithms for AI
3.1 Core Quantum Algorithms
3.2 QFT and Variational Quantum Algorithms
Module 4: Quantum Machine Learning
4.1 Algorithms for Regression and Classification
4.2 Algorithms for Dimensionality and Clustering
Module 5: Quantum Deep Learning
5.1 Algorithms for Neural Networks – Part I
5.2 Algorithms for Neural Networks – Part II
Module 6: Ethical Considerations
6.1 Ethics for Artificial Intelligence
6.2 Ethics for Quantum Computing
Module 7: Trends and Outlook
7.1 Current Trends and Tools
7.2 Future Outlook and Investment
Module 8: Use Cases & Case Studies
8.1 Quantum Use Cases
8.2 QML Case Studies
Module 9: Workshop
9.1 Project – I: QSVM for Iris Dataset
9.2 Project – II: VQC/QNN on Iris Dataset
9.3 Bonus: IBM Quantum Computers
Optional Module: AI Agents for Quantum
1. What Are AI Agents
2. Key Capabilities of AI Agents in Quantum Computing
3. Applications and Trends for AI Agents in Quantum Computing
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents
Tools you will explore
IBM Qiskit
D-Wave Leap
Google TensorFlow Quantum (TFQ)
Amazon Braket
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.930
Klassikaal
max 12
5 dagen
AI+ Quantum Practitioner™ eLearning
Formerly known as AI+ Quantum™ <br> <br>Harness Quantum Power with AI
AI + Quantum Integration: Explore Quantum Gates, Circuits, and AI applications
Advanced Learnings: Includes Quantum Deep Learning and transformative AI methodologies
Industry-Oriented: Real-world case studies and trend analysis
Ethical Focus: Learn implications of quantum AI responsibly and efficiently
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
1.1 Artificial Intelligence Refresher
1.2 Quantum Computing Refresher
Module 2: Quantum Computing Gates, Circuits, and Algorithms
2.1 Quantum Gates and their Representation
2.2 Multi Qubit Systems and Multi Qubit Gates
Module 3: Quantum Algorithms for AI
3.1 Core Quantum Algorithms
3.2 QFT and Variational Quantum Algorithms
Module 4: Quantum Machine Learning
4.1 Algorithms for Regression and Classification
4.2 Algorithms for Dimensionality and Clustering
Module 5: Quantum Deep Learning
5.1 Algorithms for Neural Networks – Part I
5.2 Algorithms for Neural Networks – Part II
Module 6: Ethical Considerations
6.1 Ethics for Artificial Intelligence
6.2 Ethics for Quantum Computing
Module 7: Trends and Outlook
7.1 Current Trends and Tools
7.2 Future Outlook and Investment
Module 8: Use Cases & Case Studies
8.1 Quantum Use Cases
8.2 QML Case Studies
Module 9: Workshop
9.1 Project – I: QSVM for Iris Dataset
9.2 Project – II: VQC/QNN on Iris Dataset
9.3 Bonus: IBM Quantum Computers
Optional Module: AI Agents for Quantum
1. What Are AI Agents
2. Key Capabilities of AI Agents in Quantum Computing
3. Applications and Trends for AI Agents in Quantum Computing
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents
Tools you will explore
IBM Qiskit
D-Wave Leap
Google TensorFlow Quantum (TFQ)
Amazon Braket
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€530
E-Learning
max 999
5 dagen
AI+ Robotics™
Build the Future with Smart Automation
AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation
Real-World Systems: Work with autonomous systems and intelligent agents
Ethics & Innovation: Learn industry-aligned practices and innovation strategies
Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions
Module 1: Introduction to Robotics and Artificial Intelligence (AI)
1.1 Overview of Robotics: Introduction, History, Evolution, and Impact
1.2 Introduction to Artificial Intelligence (AI) in Robotics
1.3 Fundamentals of Machine Learning (ML) and Deep Learning
1.4 Role of Neural Networks in Robotics
Module 2: Understanding AI and Robotics Mechanics
2.1 Components of AI Systems and Robotics
2.2 Deep Dive into Sensors, Actuators, and Control Systems
2.3 Exploring Machine Learning Algorithms in Robotics
Module 3: Autonomous Systems and Intelligent Agents
3.1 Introduction to Autonomous Systems
3.2 Building Blocks of Intelligent Agents
3.3 Case Studies: Autonomous Vehicles and Industrial Robots
3.4 Key Platforms for Development: ROS (Robot Operating System)
Module 4: AI and Robotics Development Frameworks
4.1 Python for Robotics and Machine Learning
4.2 TensorFlow and PyTorch for AI in Robotics
4.3 Introduction to Other Essential Frameworks
Module 5: Deep Learning Algorithms in Robotics
5.1 Understanding Deep Learning: Neural Networks, CNNs
5.2 Robotic Vision Systems: Object Detection, Recognition
5.3 Hands-on Session: Training a CNN for Object Recognition
5.4 Use-case: Precision Manufacturing with Robotic Vision
Module 6: Reinforcement Learning in Robotics
6.1 Basics of Reinforcement Learning (RL)
6.2 Implementing RL Algorithms for Robotics
6.3 Hands-on Session: Developing RL Models for Robots
6.4 Use-case: Optimizing Warehouse Operations with RL
Module 7: Generative AI for Robotic Creativity
7.1 Exploring Generative AI: GANs and Applications
7.2 Creative Robots: Design, Creation, and Innovation
7.3 Hands-on Session: Generating Novel Designs for Robotics
7.4 Use-case: Custom Manufacturing with AI
Module 8: Natural Language Processing (NLP) for Human-Robot Interaction
8.1 Introduction to NLP for Robotics
8.2 Voice-Activated Control Systems
8.3 Hands-on Session: Creating a Voice-command Robot Interface
8.4 Case-Study: Assistive Robots in Healthcare
Module 9: Practical Activities and Use-Cases
9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming
9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming
9.3 Hands-on Session-3: PID Controller Implementation using Python programming
9.4 Use-cases: Precision Agriculture, Automated Assembly Lines
Module 10: Emerging Technologies and Innovation in Robotics
10.1 Integration of Blockchain and Robotics
10.2 Quantum Computing and Its Potential
Module 11: Exploring AI with Robotic Process Automation
11.1 Understanding Robotic Process Automation and its use cases
11.2 Popular RPA Tools and Their Features
11.3 Integrating AI with RPA
Module 12: AI Ethics, Safety, and Policy
12.1 Ethical Considerations in AI and Robotics
12.2 Safety Standards for AI-Driven Robotics
12.3 Discussion: Navigating AI Policies and Regulations
Module 13: Innovations and Future Trends in AI and Robotics
13.1 Latest Innovations in Robotics and AI
13.2 Future of Work and Society: Impact of AI and Robotics
Optional Module: AI Agents for Robotics
1. What Are AI Agents
2. Key Capabilities of AI Agents in Robotics
3. Applications and Trends for AI Agents in Robotics
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. The Future of AI Agents in Robotics
7. Types of AI Agents
Tools you will explore
OpenAI Gym
GreyOrange
Neurala
Dialogflow
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.930
Klassikaal
max 12
5 dagen
AI+ Robotics Practitioner™ eLearning
Formerly known as AI+ Robotics™ <br> <br>Build the Future with Smart Automation
AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation
Real-World Systems: Work with autonomous systems and intelligent agents
Ethics & Innovation: Learn industry-aligned practices and innovation strategies
Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions
Module 1: Introduction to Robotics and Artificial Intelligence (AI)
1.1 Overview of Robotics: Introduction, History, Evolution, and Impact
1.2 Introduction to Artificial Intelligence (AI) in Robotics
1.3 Fundamentals of Machine Learning (ML) and Deep Learning
1.4 Role of Neural Networks in Robotics
Module 2: Understanding AI and Robotics Mechanics
2.1 Components of AI Systems and Robotics
2.2 Deep Dive into Sensors, Actuators, and Control Systems
2.3 Exploring Machine Learning Algorithms in Robotics
Module 3: Autonomous Systems and Intelligent Agents
3.1 Introduction to Autonomous Systems
3.2 Building Blocks of Intelligent Agents
3.3 Case Studies: Autonomous Vehicles and Industrial Robots
3.4 Key Platforms for Development: ROS (Robot Operating System)
Module 4: AI and Robotics Development Frameworks
4.1 Python for Robotics and Machine Learning
4.2 TensorFlow and PyTorch for AI in Robotics
4.3 Introduction to Other Essential Frameworks
Module 5: Deep Learning Algorithms in Robotics
5.1 Understanding Deep Learning: Neural Networks, CNNs
5.2 Robotic Vision Systems: Object Detection, Recognition
5.3 Hands-on Session: Training a CNN for Object Recognition
5.4 Use-case: Precision Manufacturing with Robotic Vision
Module 6: Reinforcement Learning in Robotics
6.1 Basics of Reinforcement Learning (RL)
6.2 Implementing RL Algorithms for Robotics
6.3 Hands-on Session: Developing RL Models for Robots
6.4 Use-case: Optimizing Warehouse Operations with RL
Module 7: Generative AI for Robotic Creativity
7.1 Exploring Generative AI: GANs and Applications
7.2 Creative Robots: Design, Creation, and Innovation
7.3 Hands-on Session: Generating Novel Designs for Robotics
7.4 Use-case: Custom Manufacturing with AI
Module 8: Natural Language Processing (NLP) for Human-Robot Interaction
8.1 Introduction to NLP for Robotics
8.2 Voice-Activated Control Systems
8.3 Hands-on Session: Creating a Voice-command Robot Interface
8.4 Case-Study: Assistive Robots in Healthcare
Module 9: Practical Activities and Use-Cases
9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming
9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming
9.3 Hands-on Session-3: PID Controller Implementation using Python programming
9.4 Use-cases: Precision Agriculture, Automated Assembly Lines
Module 10: Emerging Technologies and Innovation in Robotics
10.1 Integration of Blockchain and Robotics
10.2 Quantum Computing and Its Potential
Module 11: Exploring AI with Robotic Process Automation
11.1 Understanding Robotic Process Automation and its use cases
11.2 Popular RPA Tools and Their Features
11.3 Integrating AI with RPA
Module 12: AI Ethics, Safety, and Policy
12.1 Ethical Considerations in AI and Robotics
12.2 Safety Standards for AI-Driven Robotics
12.3 Discussion: Navigating AI Policies and Regulations
Module 13: Innovations and Future Trends in AI and Robotics
13.1 Latest Innovations in Robotics and AI
13.2 Future of Work and Society: Impact of AI and Robotics
Optional Module: AI Agents for Robotics
1. What Are AI Agents
2. Key Capabilities of AI Agents in Robotics
3. Applications and Trends for AI Agents in Robotics
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. The Future of AI Agents in Robotics
7. Types of AI Agents
Tools you will explore
OpenAI Gym
GreyOrange
Neurala
Dialogflow
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€530
E-Learning
max 999
5 dagen
AI+ Data Agent Specialty™
's-Hertogenbosch
do 5 nov. 2026
Formerly known as AI+ Data Agent™ <br> <br> Empower businesses with AI + Data Agent Specialty™ to unlock insights, automate analytics, and drive smarter decisions.
Empowering the Future with AI+ Data Agent Specialty™: Shaping Smarter Decision-Makers
Beginner-Friendly Certification: Perfect entry point to understand data-driven AI concepts and automation tools
Comprehensive Foundation: Explores AI data handling, analytics, and real-world business insights
Open to All: Designed for learners with curiosity about using data and AI for smarter outcomes
Module 1: Introduction to AI Agents
1.1 What is an AI Agent?
1.2 Components of AI Agents
1.3 Types of AI Agents
1.4 Hands on: No-Code AI and Machine Learning Models for Data Agents
Module 2: Data Agents and Their Role in AI Systems
2.1 AI Data Agents
2.2 AI vs. AI Data Agent
2.3 Components of AI Data Agents
2.4 Types of AI Data Agents
2.5 Existing AI Data Agents in Trend
Module 3: Data Collection and Acquisition for AI Data Agents
3.1 Steps in AI Data Collection Structure & Plan
3.2 Methods of Data Collection
Module 4: Data Pre-processing and Feature Engineering
4.1 Data Cleaning and Transformation
4.2 Feature Engineering for AI Models
4.3 No-Code AI Data Agent for Preprocessing & Feature Engineering
Module 5: AI and Machine Learning Models for Data Agents
5.1 Introduction to Machine Learning Models for Data Agents
5.2 Model Selection and Training
5.3 Hands on: No-Code AI and Machine Learning Models for Data Agents
Module 6: Ethics, Security, and Privacy in AI Data Agents
6.1 Ethical Considerations in AI Data Agents
6.2 Security and Privacy Concerns
Module 7: Capstone Project
7.1 Problem Statement
7.2 Practical Implementation
7.3 Evaluation and Optimization
7.4 No-Code AI and Machine Learning Models for Data Agents
Tools you will explore
Python
TensorFlow
PyTorch
Scikit-learn
Keras
LangChain
Hugging Face Transformers
Jupyter Notebooks
Power BI
Tableau
Pandas
NumPy
SQL
Apache Spark
Airflow
DataBricks
RESTful APIs
Matplotlib
Data Visualization & Automation Tools
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+ Data Agent Specialty™ eLearning
Formerly known as AI+ Data Agent™ <br> <br> Empower businesses with AI + Data Agent Specialty™ to unlock insights, automate analytics, and drive smarter decisions.
Empowering the Future with AI+ Data Agent Specialty™: Shaping Smarter Decision-Makers
Beginner-Friendly Certification: Perfect entry point to understand data-driven AI concepts and automation tools
Comprehensive Foundation: Explores AI data handling, analytics, and real-world business insights
Open to All: Designed for learners with curiosity about using data and AI for smarter outcomes
Module 1: Introduction to AI Agents
1.1 What is an AI Agent?
1.2 Components of AI Agents
1.3 Types of AI Agents
1.4 Hands on: No-Code AI and Machine Learning Models for Data Agents
Module 2: Data Agents and Their Role in AI Systems
2.1 AI Data Agents
2.2 AI vs. AI Data Agent
2.3 Components of AI Data Agents
2.4 Types of AI Data Agents
2.5 Existing AI Data Agents in Trend
Module 3: Data Collection and Acquisition for AI Data Agents
3.1 Steps in AI Data Collection Structure & Plan
3.2 Methods of Data Collection
Module 4: Data Pre-processing and Feature Engineering
4.1 Data Cleaning and Transformation
4.2 Feature Engineering for AI Models
4.3 No-Code AI Data Agent for Preprocessing & Feature Engineering
Module 5: AI and Machine Learning Models for Data Agents
5.1 Introduction to Machine Learning Models for Data Agents
5.2 Model Selection and Training
5.3 Hands on: No-Code AI and Machine Learning Models for Data Agents
Module 6: Ethics, Security, and Privacy in AI Data Agents
6.1 Ethical Considerations in AI Data Agents
6.2 Security and Privacy Concerns
Module 7: Capstone Project
7.1 Problem Statement
7.2 Practical Implementation
7.3 Evaluation and Optimization
7.4 No-Code AI and Machine Learning Models for Data Agents
Tools you will explore
Python
TensorFlow
PyTorch
Scikit-learn
Keras
LangChain
Hugging Face Transformers
Jupyter Notebooks
Power BI
Tableau
Pandas
NumPy
SQL
Apache Spark
Airflow
DataBricks
RESTful APIs
Matplotlib
Data Visualization & Automation Tools
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+ Network™
Master the Future of Networking: Harness AI for Automation, Security, and Next-Generation Efficiency
This course provides professionals with the basic knowledge and advanced skills needed to understand the combination of artificial intelligence and current networking technologies. It discusses fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Important focus areas consist of AI-powered network automation, orchestration, and security upgrades, combined with hands-on projects and practical labs for real-life implementation. The class ends by examining new developments and
upcoming pathways in AI-enhanced networking, getting students ready for leading positions in this quickly changing sector.
Module 1: Networking Foundations
1.1 Basic Networking Concepts
1.2 Networking Protocols and Standards
1.3 Network Infrastructure and Design
1.4 Introduction to Network Security
Module 2: Advanced Networking Technologies
2.1 Network Virtualization and Cloud Networking
2.2 Emerging Network Architectures
2.3 Advanced Routing and Switching
2.4 Network Storage and Data Centers
Module 3: AI in Networking
3.1 Introduction to AI and Machine Learning
3.2 AI-Driven Network Optimization
3.3 AI for Network Security and Threat Detection
3.4 AI-Enhanced Network Management
Module 4: Network Automation and Orchestration
4.1 Fundamentals of Network Automation
4.2 AI-Driven Network Orchestration
4.3 Policy-Driven Network Management
4.4 Case Studies in Network Automation
Module 5: AI-Enhanced Network Security
5.1 Advanced Threat Detection with AI
5.2 Secure Network Design and Architecture
5.3 AI for Cybersecurity Intelligence
5.4 Ethical Considerations in AI-Driven Security
Module 6: Practical Labs and Hands-On Projects
6.1 Network Simulation and Emulation
6.2 AI-Driven Network Automation Projects
6.3 AI for Network Security Projects
6.4 Capstone Project (Using Google Colab and Azure cloud)
Module 7: Emerging Trends and Future Directions
7.1 Future of AI in Networking
7.2 AI-Powered IoT Networks
7.3 Blockchain and AI in Networking
7.4 Continuous Learning and Career Development
Optional Module: AI Agents for Network Management
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
Elastic
Juniper
Netdata
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.930
Klassikaal
max 12
5 dagen
AI+ Network™ eLearning
Master the Future of Networking: Harness AI for Automation, Security, and Next-Generation Efficiency
This course provides professionals with the basic knowledge and advanced skills needed to understand the combination of artificial intelligence and current networking technologies. It discusses fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Important focus areas consist of AI-powered network automation, orchestration, and security upgrades, combined with hands-on projects and practical labs for real-life implementation. The class ends by examining new developments and
upcoming pathways in AI-enhanced networking, getting students ready for leading positions in this quickly changing sector.
Module 1: Networking Foundations
1.1 Basic Networking Concepts
1.2 Networking Protocols and Standards
1.3 Network Infrastructure and Design
1.4 Introduction to Network Security
Module 2: Advanced Networking Technologies
2.1 Network Virtualization and Cloud Networking
2.2 Emerging Network Architectures
2.3 Advanced Routing and Switching
2.4 Network Storage and Data Centers
Module 3: AI in Networking
3.1 Introduction to AI and Machine Learning
3.2 AI-Driven Network Optimization
3.3 AI for Network Security and Threat Detection
3.4 AI-Enhanced Network Management
Module 4: Network Automation and Orchestration
4.1 Fundamentals of Network Automation
4.2 AI-Driven Network Orchestration
4.3 Policy-Driven Network Management
4.4 Case Studies in Network Automation
Module 5: AI-Enhanced Network Security
5.1 Advanced Threat Detection with AI
5.2 Secure Network Design and Architecture
5.3 AI for Cybersecurity Intelligence
5.4 Ethical Considerations in AI-Driven Security
Module 6: Practical Labs and Hands-On Projects
6.1 Network Simulation and Emulation
6.2 AI-Driven Network Automation Projects
6.3 AI for Network Security Projects
6.4 Capstone Project (Using Google Colab and Azure cloud)
Module 7: Emerging Trends and Future Directions
7.1 Future of AI in Networking
7.2 AI-Powered IoT Networks
7.3 Blockchain and AI in Networking
7.4 Continuous Learning and Career Development
Optional Module: AI Agents for Network Management
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
Elastic
Juniper
Netdata
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€530
E-Learning
max 999
5 dagen
AI+ Business Intelligence Practitioner™
's-Hertogenbosch
ma 23 nov. 2026
Formerly known as AI+ Business Intelligence™<br><br>Empower Your Career with AI+ Business Intelligence Practitioner™ for Advanced Data Solutions
AI-Powered Business Intelligence: Leverage advanced tools to turn raw data into actionable insights
Smarter Decision-Making: Make faster, data-driven decisions that align with business objectives
Strategic Growth: Identify trends, opportunities, and risks to drive sustainable growth
Data-Driven Innovation: Empower your strategy with predictive analytics and data-informed decisions
Module 1: Introduction to AI and BI Fundamentals
1.1 Overview of AI and BI Integration
1.2 Core Concepts in Business Intelligence
1.3 Data Analysis Process and AI’s Role
1.4 BI Trends and Challenges
1.5 Case Study
1.6. Hands on Activity
Module 2: Python for AI-Driven Business Intelligence
2.1 Python Programming Fundamentals
2.2 Advanced Python Libraries for BI
2.3 Visualization with Python
2.4 Hands on Activity
Module 3: Data Preparation and Feature Engineering with AI
3.1 Data Collection Techniques
3.2 Data Quality & Evaluation
3.3 Advanced Data Preparation
3.4 Hands on Activity
Module 4: Machine Learning (ML) for Business Intelligence
4.1 ML Models for BI
4.2 Hands on Activity
Module 5: Advanced AI and Generative AI for BI
5.1 Deep Learning and Neural Networks for BI
5.2 Generative AI for BI
5.3 Hands on Activity
Module 6: Statistical Analysis with AI Tools
6.1 Statistical Analysis for BI
6.2 Time Series Analysis
6.3 Hands on Activity
Module 7: AI-Powered Business Intelligence Tools
7.1 AI in BI Platforms
7.2 Power BI Essentials
7.3 Tableau Essentials
7.4 Hands on Activity
Module 8: Prompt Engineering for AI-Driven BI
8.1 Introduction to Prompt Engineering
8.2 Crafting Effective Prompts
8.3 Hands on Activity
Module 9: Communication Skills
9.1 Data Storytelling & Communication
9.2 Solution Presentation
Module 10: Capstone Project
10.1 Capstone Project 1
10.2 Capstone Project 2
10.3 Capstone Project 3
Tools you will explore
Scikit-learn
TensorFlow
ChatGPT
Jupyter Notebooks
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.930
Klassikaal
max 12
5 dagen
AI+ Business Intelligence Practitioner™ eLearning
Formerly known as AI+ Business Intelligence™<br><br>Empower Your Career with AI+ Business Intelligence Practitioner™ for Advanced Data Solutions
AI-Powered Business Intelligence: Leverage advanced tools to turn raw data into actionable insights
Smarter Decision-Making: Make faster, data-driven decisions that align with business objectives
Strategic Growth: Identify trends, opportunities, and risks to drive sustainable growth
Data-Driven Innovation: Empower your strategy with predictive analytics and data-informed decisions
Module 1: Introduction to AI and BI Fundamentals
1.1 Overview of AI and BI Integration
1.2 Core Concepts in Business Intelligence
1.3 Data Analysis Process and AI’s Role
1.4 BI Trends and Challenges
1.5 Case Study
1.6. Hands on Activity
Module 2: Python for AI-Driven Business Intelligence
2.1 Python Programming Fundamentals
2.2 Advanced Python Libraries for BI
2.3 Visualization with Python
2.4 Hands on Activity
Module 3: Data Preparation and Feature Engineering with AI
3.1 Data Collection Techniques
3.2 Data Quality & Evaluation
3.3 Advanced Data Preparation
3.4 Hands on Activity
Module 4: Machine Learning (ML) for Business Intelligence
4.1 ML Models for BI
4.2 Hands on Activity
Module 5: Advanced AI and Generative AI for BI
5.1 Deep Learning and Neural Networks for BI
5.2 Generative AI for BI
5.3 Hands on Activity
Module 6: Statistical Analysis with AI Tools
6.1 Statistical Analysis for BI
6.2 Time Series Analysis
6.3 Hands on Activity
Module 7: AI-Powered Business Intelligence Tools
7.1 AI in BI Platforms
7.2 Power BI Essentials
7.3 Tableau Essentials
7.4 Hands on Activity
Module 8: Prompt Engineering for AI-Driven BI
8.1 Introduction to Prompt Engineering
8.2 Crafting Effective Prompts
8.3 Hands on Activity
Module 9: Communication Skills
9.1 Data Storytelling & Communication
9.2 Solution Presentation
Module 10: Capstone Project
10.1 Capstone Project 1
10.2 Capstone Project 2
10.3 Capstone Project 3
Tools you will explore
Scikit-learn
TensorFlow
ChatGPT
Jupyter Notebooks
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€530
E-Learning
max 999
5 dagen