Opleidingen
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AI+ Engineer Practitioner™ eLearning
Formerly known as AI+ Engineer™
Innovate Engineering: Leverage AI-Driven Smart Solutions
Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks
Tool Proficiency: Includes Transfer Learning with Hugging Face and GUI design
Deployment Focus: Build real AI systems and manage communication pipelines
Practical Mastery: Gain the skills to engineer scalable AI solutions for innovation
AI & Software Engineers: Enhance your development skills by mastering AI techniques and designing advanced AI systems.
Machine Learning Enthusiasts: Apply deep learning, neural networks, and NLP techniques to real-world AI challenges.
Data Scientists: Strengthen your AI toolkit with engineering techniques for building and deploying scalable AI solutions.
IT Specialists & System Architects: Integrate AI solutions into existing infrastructures, optimizing performance and scalability.
Students & New Graduates: Develop in-demand AI engineering skills and prepare for a successful career in the rapidly growing AI field.
Master AI System Design:Develop the skills to design, implement, and optimize advanced AI systems for real-world applications.
Build Scalable AI Solutions:Learn how to create scalable AI solutions for industries like technology, finance, and healthcare.
Tackle Complex Engineering Challenges:This certification ensures you’re equipped to solve challenges in AI architecture, neural networks, and NLP.
Contribute to AI-Driven Innovations:Certified AI+ Engineer Practitioner™ develop cutting-edge AI solutions that enhance business operations and drive future innovations.
Advance Your Career in AI Engineering:As demand for skilled AI engineers rises, this certification offers a competitive advantage in the job market.
Course Overview
Course Introduction Preview
Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI Preview
1.2 Core Concepts and Techniques in AI Preview
1.3 Ethical Considerations
Module 2: Introduction to AI Architecture
2.1 Overview of AI and its Various ApplicationsPreview
2.2 Introduction to AI Architecture Preview
2.3 Understanding the AI Development Lifecycle Preview
2.4 Hands-on: Setting up a Basic AI Environment
Module 3: Fundamentals of Neural Networks
3.1 Basics of Neural Networks Preview
3.2 Activation Functions and Their Role Preview
3.3 Backpropagation and Optimization Algorithms
3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
Module 4: Applications of Neural Networks
4.1 Introduction to Neural Networks in Image Processing
4.2 Neural Networks for Sequential Data
4.3 Practical Implementation of Neural Networks
Module 5: Significance of Large Language Models (LLM)
5.1 Exploring Large Language Models
5.2 Popular Large Language Models
5.3 Practical Finetuning of Language Models
5.4 Hands-on: Practical Finetuning for Text Classification
Module 6: Application of Generative AI
6.1 Introduction to Generative Adversarial Networks (GANs)
6.2 Applications of Variational Autoencoders (VAEs)
6.3 Generating Realistic Data Using Generative Models
6.4 Hands-on: Implementing Generative Models for Image Synthesis
Module 7: Natural Language Processing
7.1 NLP in Real-world Scenarios
7.2 Attention Mechanisms and Practical Use of Transformers
7.3 In-depth Understanding of BERT for Practical NLP Tasks
7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models
Module 8: Transfer Learning with Hugging Face
8.1 Overview of Transfer Learning in AI
8.2 Transfer Learning Strategies and Techniques
8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks
Module 9: Crafting Sophisticated GUIs for AI Solutions
9.1 Overview of GUI-based AI Applications
9.2 Web-based Framework
9.3 Desktop Application Framework
Module 10: AI Communication and Deployment Pipeline
10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
10.2 Building a Deployment Pipeline for AI Models
10.3 Developing Prototypes Based on Client Requirements
10.4 Hands-on: Deployment
Optional Module: AI Agents for Engineering
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
TensorFlow
Hugging Face Transformers
Jenkins
TensorFlow Hub
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€530
E-Learning
max 999
5 dagen
AI+ Quantum Practitioner™
Formerly known as AI+ Quantum™
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
Quantum Computing Engineers: Enhance quantum system design and performance using AI for optimization and control.
Physics Engineers: Apply AI techniques to improve quantum simulations and computational models.
AI Specialists: Leverage AI and quantum algorithms to create intelligent solutions for complex problems.
IT Specialists & System Integrators: Integrate AI-driven quantum computing systems to optimize infrastructure and solve large-scale challenges.
Students & New Graduates: Gain foundational skills in AI and quantum computing to excel in the rapidly advancing quantum technology field.
Demand for AI and Quantum Technology Experts:Organizations are seeking certified experts who can integrate AI with quantum technologies to optimize data processing and accelerate problem-solving.
Mitigating Risks in AI and Quantum Integration:Mismanagement of quantum computing systems and AI integration can result in inefficiencies and inaccurate results in critical applications.
Developing Reliable Quantum Strategies with AI:Certified professionals play a key role in developing quantum strategies that ensure performance, reliability, and alignment with industry standards.
Gaining a Competitive Edge:As quantum computing and AI continue to revolutionize industries, this certification provides professionals with a competitive edge, preparing them for advanced roles.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€3.930
Klassikaal
max 12
5 dagen
AI+ Quantum Practitioner™ eLearning
Formerly known as AI+ Quantum™
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
Quantum Computing Engineers: Enhance quantum system design and performance using AI for optimization and control.
Physics Engineers: Apply AI techniques to improve quantum simulations and computational models.
AI Specialists: Leverage AI and quantum algorithms to create intelligent solutions for complex problems.
IT Specialists & System Integrators: Integrate AI-driven quantum computing systems to optimize infrastructure and solve large-scale challenges.
Students & New Graduates: Gain foundational skills in AI and quantum computing to excel in the rapidly advancing quantum technology field.
Demand for AI and Quantum Technology Experts:Organizations are seeking certified experts who can integrate AI with quantum technologies to optimize data processing and accelerate problem-solving.
Mitigating Risks in AI and Quantum Integration:Mismanagement of quantum computing systems and AI integration can result in inefficiencies and inaccurate results in critical applications.
Developing Reliable Quantum Strategies with AI:Certified professionals play a key role in developing quantum strategies that ensure performance, reliability, and alignment with industry standards.
Gaining a Competitive Edge:As quantum computing and AI continue to revolutionize industries, this certification provides professionals with a competitive edge, preparing them for advanced roles.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€530
E-Learning
max 999
5 dagen
AI+ Robotics Practitioner™
Formerly known as 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
Robotics Engineers Enhance robotic system design and functionality using AI for automation and control.
Mechanical Engineers: Integrate AI to optimize robotics systems and improve performance in manufacturing and production.
AI Specialists: Apply AI techniques to enhance the intelligence and autonomy of robotic systems.
IT Specialists & System Integrators: Implement AI-powered solutions to improve robotics infrastructure and communication systems.
Students & New Graduates: Build essential skills in AI and robotics to succeed in an emerging field with endless growth potential.
Demand for Certified AI & Robotics Professionals:Organizations are seeking certified professionals who can integrate AI into robotics to optimize processes, enhance automation, and improve operational efficiency.
Risks of Mismanaging AI & Robotics:Mismanagement of robotic systems and AI technologies can lead to operational inefficiencies and safety risks.
Role of Certification in Robotics Strategy:Certified professionals are key in developing robotics strategies that maximize performance, safety, and compliance with industry regulations.
Career Advantage & Leadership Opportunities:As robotics and AI continue to reshape industries, this certification offers professionals a distinct advantage, positioning them for leadership roles.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€3.930
Klassikaal
max 12
5 dagen
AI+ Robotics Practitioner™ eLearning
Formerly known as 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
Robotics Engineers Enhance robotic system design and functionality using AI for automation and control.
Mechanical Engineers: Integrate AI to optimize robotics systems and improve performance in manufacturing and production.
AI Specialists: Apply AI techniques to enhance the intelligence and autonomy of robotic systems.
IT Specialists & System Integrators: Implement AI-powered solutions to improve robotics infrastructure and communication systems.
Students & New Graduates: Build essential skills in AI and robotics to succeed in an emerging field with endless growth potential.
Demand for Certified AI & Robotics Professionals:Organizations are seeking certified professionals who can integrate AI into robotics to optimize processes, enhance automation, and improve operational efficiency.
Risks of Mismanaging AI & Robotics:Mismanagement of robotic systems and AI technologies can lead to operational inefficiencies and safety risks.
Role of Certification in Robotics Strategy:Certified professionals are key in developing robotics strategies that maximize performance, safety, and compliance with industry regulations.
Career Advantage & Leadership Opportunities:As robotics and AI continue to reshape industries, this certification offers professionals a distinct advantage, positioning them for leadership roles.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€530
E-Learning
max 999
5 dagen
AI+ Data Agent Specialty™
's-Hertogenbosch
di 1 dec. 2026
Formerly known as AI+ Data Agent™
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
Data Professionals: Ideal for data analysts, engineers, and scientists looking to enhance their expertise with AI-driven automation.
Business Strategists: Perfect for professionals aiming to leverage data intelligence for informed and strategic decision-making.
AI Enthusiasts: Designed for learners passionate about integrating AI with data management and intelligent systems.
Tech Innovators: Suited for individuals exploring automation, predictive analytics, and autonomous data solutions.
Aspiring Leaders: Ideal for professionals seeking to lead AI transformation initiatives and drive data-driven innovation within their organizations.
Comprehensive Skill DevelopmentMaster AI-driven data analysis, automation, and intelligent agent design for real-world business applications.
Industry RecognitionEarn a globally recognized certification that validates your expertise in AI-powered data management and analytics.
Hands-On LearningEngage in practical projects that simulate real data environments, enhancing problem-solving and analytical decision-making skills.
Career AdvancementOpen doors to roles in data engineering, AI analytics, and intelligent automation across multiple industries.
Future-Ready ExpertiseStay ahead in the evolving AI landscape with cutting-edge knowledge of data agents and autonomous systems.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€995
Klassikaal
max 12
1 dag
AI+ Data Agent Specialty™ eLearning
Formerly known as AI+ Data Agent™
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
Data Professionals: Ideal for data analysts, engineers, and scientists looking to enhance their expertise with AI-driven automation.
Business Strategists: Perfect for professionals aiming to leverage data intelligence for informed and strategic decision-making.
AI Enthusiasts: Designed for learners passionate about integrating AI with data management and intelligent systems.
Tech Innovators: Suited for individuals exploring automation, predictive analytics, and autonomous data solutions.
Aspiring Leaders: Ideal for professionals seeking to lead AI transformation initiatives and drive data-driven innovation within their organizations.
Comprehensive Skill DevelopmentMaster AI-driven data analysis, automation, and intelligent agent design for real-world business applications.
Industry RecognitionEarn a globally recognized certification that validates your expertise in AI-powered data management and analytics.
Hands-On LearningEngage in practical projects that simulate real data environments, enhancing problem-solving and analytical decision-making skills.
Career AdvancementOpen doors to roles in data engineering, AI analytics, and intelligent automation across multiple industries.
Future-Ready ExpertiseStay ahead in the evolving AI landscape with cutting-edge knowledge of data agents and autonomous systems.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€225
E-Learning
max 999
1 dag
AI+ Network Practitioner™
Validate Your Expertise in Networking: Harness AI for Automation, Security, and Next-Generation Efficiency
This certification validates professional knowledge and competency in the combination of artificial intelligence and current networking technologies. The exam assesses understanding of fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Key focus areas include AI-powered network automation, orchestration, and security upgrades. The exam includes scenario-based questions covering emerging developments in AI-enhanced networking, validating candidate readiness for leadership roles in this rapidly evolving sector.
Networking Professionals: Looking to advance your skills by integrating AI into network design, automation, and security to stay ahead in a competitive field.
AI Enthusiasts: Interested in applying AI technologies like ML and automation Specifically, networking domains.
IT Specialists: Focused on exploring the intersection of cloud computing, IoT, and AI to optimize infrastructure and network performance.
Students and Fresh Graduates: Pursuing careers in AI, cybersecurity, or networking and wanting to gain hands-on experience and credentials to strengthen their professional profile.
Cybersecurity Analysts: Seeking to utilize AI-driven solutions for threat detection, network protection, and predictive analytics.
System Administrators: Eager to transition into roles involving network automation, orchestration, and AI-based tools for managing complex systems.
Tech Innovators and Researchers: Interested in exploring emerging trends like 5G, edge computing, or blockchain in AI-enhanced networking.
Comprehensive KnowledgeCovers fundamental networking concepts and advanced AI-driven technologies like SDN and NFV.
AI-Powered EfficiencyExam assesses knowledge of how AI can optimize network performance, automation, and security
Practical Application AssessmentExam includes scenario-based questions for real-world application of AI in networking.
Future-Ready SkillsValidates competency to adapt to the rapidly evolving AI-enhanced networking landscape.
Leadership OpportunitiesValidates competency for leadership roles in the AI and networking fields.
Module 1: Enterprise Networking Foundations & AI Workload Impact
1.1 Basic Networking Concepts
1.2 Network Infrastructure and Design
1.3 Introduction to Network Security
1.4 AI Workload Networking Overview
Module 2: Advanced Routing, Switching, and Data Center/AI Fabric Networking
2.1 Advanced Routing and Switching
2.2 Data Center and AI Infrastructure Networking
2.3 High-Performance AI Fabric Considerations
2.4 Quality of Service (QoS) for Application and AI Workloads
Module 3: Cloud Networking, SASE, and Hybrid Connectivity
3.1 Network Virtualization and Cloud Networking Models
3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
3.3 SASE and SSE with AI
Module 4: Wi-Fi 7, Edge AI & IoT Networking
4.1 Wi-Fi 7 and AI-Driven RF Optimization
4.2 Edge Computing, Fog Networking and IoT Models
4.3 Edge AI and Small Language Models
4.4 Wi-Fi 7 + Edge AI Use Cases and Architecture
Module 5: AI & Machine Learning Foundations for Network Engineers
5.1 AI and Machine Learning Fundamentals
5.2 AI-Driven Network Optimization
5.3 Operational Limits of AI Recommendations
5.4 Predictive Network Maintenance
Module 6: Generative AI, RAG, and Prompt Engineering for Operations
6.1 Generative AI and LLM Concepts for Network Operations
6.2 RAG (Retrieval-Augmented Generation) for Network Knowledge
6.3 Prompt Engineering for Network Engineers
Module 7: Network Automation, IaC, and Agentic AI Workflows
7.1 Fundamentals of Network Automation & Infrastructure as Code (IaC)
7.2 Network APIs and Programmability
7.3 Agentic AI, Function Calling, and MCP
7.4 ChatOps and Operational Workflows
7.5 Use-Cases and Case Studies
Module 8: AI-Enhanced Network Security and Zero Trust
8.1 AI-Enhanced Threat Detection
8.2 Secure Network Design and Zero Trust
8.3 SIEM, SOC, and AI-Assisted Security Operations
8.4 Adversarial AI and AI Security Risks
8.5 Use-Cases and Case Studies
Module 9: Modern Observability: eBPF, OpenTelemetry, and AIOps
9.1 Modern Observability Foundations (Metrics, Logs, and Traces)
9.2 eBPF for Deep Network Visibility
9.3 OpenTelemetry and Streaming Telemetry Standards
9.4 AIOps: Alert Correlation, Noise Reduction, and Root Cause Support
9.5 Use-Cases and Case Studies
Module 10: AI Governance, Responsible AI, and Sustainable Networking
10.1 AI Governance and Responsible Network Operations
10.2 Privacy, Data Handling, and Bias in Network AI
10.3 Sustainable/Green Networking with AI
10.4 Future Network Operations
10.5 Use-Cases and Case Studies
Module 11: Capstone Project - End-to-End AI Network Operations
11.1 Capstone Objective
11.2 Capstone Scenario
Optional Module: Optional Module: AI Agents For Network
1.1 What Are AI Agents
1.2 Applications and Trends of AI Agents in Network Intelligence
1.3 How Does an AI Agent Work
1.4 Characteristics of AI Agents
1.5 Types of AI Agents
Tools you will explore
Ansible
Puppet
Chef
REST APIs
NETCONF
Kubernetes
OpenStack
GNS3
Cisco Packet Tracer
VMware
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€3.930
Klassikaal
max 12
5 dagen
AI+ Network Practitioner™ eLearning
Validate Your Expertise in Networking: Harness AI for Automation, Security, and Next-Generation Efficiency
This certification validates professional knowledge and competency in the combination of artificial intelligence and current networking technologies. The exam assesses understanding of fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Key focus areas include AI-powered network automation, orchestration, and security upgrades. The exam includes scenario-based questions covering emerging developments in AI-enhanced networking, validating candidate readiness for leadership roles in this rapidly evolving sector.
Networking Professionals: Looking to advance your skills by integrating AI into network design, automation, and security to stay ahead in a competitive field.
AI Enthusiasts: Interested in applying AI technologies like ML and automation Specifically, networking domains.
IT Specialists: Focused on exploring the intersection of cloud computing, IoT, and AI to optimize infrastructure and network performance.
Students and Fresh Graduates: Pursuing careers in AI, cybersecurity, or networking and wanting to gain hands-on experience and credentials to strengthen their professional profile.
Cybersecurity Analysts: Seeking to utilize AI-driven solutions for threat detection, network protection, and predictive analytics.
System Administrators: Eager to transition into roles involving network automation, orchestration, and AI-based tools for managing complex systems.
Tech Innovators and Researchers: Interested in exploring emerging trends like 5G, edge computing, or blockchain in AI-enhanced networking.
Comprehensive KnowledgeCovers fundamental networking concepts and advanced AI-driven technologies like SDN and NFV.
AI-Powered EfficiencyExam assesses knowledge of how AI can optimize network performance, automation, and security
Practical Application AssessmentExam includes scenario-based questions for real-world application of AI in networking.
Future-Ready SkillsValidates competency to adapt to the rapidly evolving AI-enhanced networking landscape.
Leadership OpportunitiesValidates competency for leadership roles in the AI and networking fields.
Module 1: Enterprise Networking Foundations & AI Workload Impact
1.1 Basic Networking Concepts
1.2 Network Infrastructure and Design
1.3 Introduction to Network Security
1.4 AI Workload Networking Overview
Module 2: Advanced Routing, Switching, and Data Center/AI Fabric Networking
2.1 Advanced Routing and Switching
2.2 Data Center and AI Infrastructure Networking
2.3 High-Performance AI Fabric Considerations
2.4 Quality of Service (QoS) for Application and AI Workloads
Module 3: Cloud Networking, SASE, and Hybrid Connectivity
3.1 Network Virtualization and Cloud Networking Models
3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
3.3 SASE and SSE with AI
Module 4: Wi-Fi 7, Edge AI & IoT Networking
4.1 Wi-Fi 7 and AI-Driven RF Optimization
4.2 Edge Computing, Fog Networking and IoT Models
4.3 Edge AI and Small Language Models
4.4 Wi-Fi 7 + Edge AI Use Cases and Architecture
Module 5: AI & Machine Learning Foundations for Network Engineers
5.1 AI and Machine Learning Fundamentals
5.2 AI-Driven Network Optimization
5.3 Operational Limits of AI Recommendations
5.4 Predictive Network Maintenance
Module 6: Generative AI, RAG, and Prompt Engineering for Operations
6.1 Generative AI and LLM Concepts for Network Operations
6.2 RAG (Retrieval-Augmented Generation) for Network Knowledge
6.3 Prompt Engineering for Network Engineers
Module 7: Network Automation, IaC, and Agentic AI Workflows
7.1 Fundamentals of Network Automation & Infrastructure as Code (IaC)
7.2 Network APIs and Programmability
7.3 Agentic AI, Function Calling, and MCP
7.4 ChatOps and Operational Workflows
7.5 Use-Cases and Case Studies
Module 8: AI-Enhanced Network Security and Zero Trust
8.1 AI-Enhanced Threat Detection
8.2 Secure Network Design and Zero Trust
8.3 SIEM, SOC, and AI-Assisted Security Operations
8.4 Adversarial AI and AI Security Risks
8.5 Use-Cases and Case Studies
Module 9: Modern Observability: eBPF, OpenTelemetry, and AIOps
9.1 Modern Observability Foundations (Metrics, Logs, and Traces)
9.2 eBPF for Deep Network Visibility
9.3 OpenTelemetry and Streaming Telemetry Standards
9.4 AIOps: Alert Correlation, Noise Reduction, and Root Cause Support
9.5 Use-Cases and Case Studies
Module 10: AI Governance, Responsible AI, and Sustainable Networking
10.1 AI Governance and Responsible Network Operations
10.2 Privacy, Data Handling, and Bias in Network AI
10.3 Sustainable/Green Networking with AI
10.4 Future Network Operations
10.5 Use-Cases and Case Studies
Module 11: Capstone Project - End-to-End AI Network Operations
11.1 Capstone Objective
11.2 Capstone Scenario
Optional Module: Optional Module: AI Agents For Network
1.1 What Are AI Agents
1.2 Applications and Trends of AI Agents in Network Intelligence
1.3 How Does an AI Agent Work
1.4 Characteristics of AI Agents
1.5 Types of AI Agents
Tools you will explore
Ansible
Puppet
Chef
REST APIs
NETCONF
Kubernetes
OpenStack
GNS3
Cisco Packet Tracer
VMware
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€530
E-Learning
max 999
5 dagen
AI+ Business Intelligence Practitioner™
's-Hertogenbosch
ma 15 feb. 2027
Formerly known as AI+ Business Intelligence™
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
Business Analysts: Sharpen your skills in data-driven strategies and AI-enhanced business intelligence for improved analysis.
Data Professionals: Expand your expertise by merging AI with business intelligence to extract actionable insights.
Executives and Leaders: Leverage AI and BI to craft smarter business strategies and boost organizational performance.
Tech Enthusiasts: Explore the intersection of AI and business intelligence and understand how these technologies are transforming industries.
Consultants: Add value to your clients by implementing AI and BI solutions to optimize business outcomes.
Comprehensive Learning Experience:Learn essential AI and BI concepts covering data preparation, predictive modeling, deep learning, and generative AI to strengthen your value in data-driven organizations.
Learn High-Demand Skills:Master Python, Power BI, Tableau, and machine learning techniques through practical exercises that prepare you to apply these tools in real-world scenarios.
Real-World Applications:Apply your knowledge through hands-on activities and a capstone project that strengthens your ability to make smarter, data-driven business decisions confidently.
Stay Ahead in the Industry:Gain expertise that positions you competitively in fast-growing AI and BI fields, helping you adapt and thrive in evolving business landscapes.
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
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.
Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.
€3.930
Klassikaal
max 12
5 dagen