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AI+ Ethical Hacker Practitioner™ eLearning
Protect Digital Landscapes: Harness AI-Enhanced Technologies
The AI+ Ethical Hacker Practitioner™ certification validates knowledge of the intersection of cybersecurity and artificial intelligence, a pivotal juncture in an era of rapid technological progress. Designed for cybersecurity professionals and ethical hacking practitioners, it assesses comprehensive knowledge of AI’s impact on digital offense and defense strategies. Unlike conventional ethical hacking certifications, this certification validates competency in applying AI techniques to enhance cybersecurity approaches. It is intended for professionals seeking to validate expertise in the integration of advanced AI methods with ethical hacking practices in a rapidly evolving digital landscape.
Cybersecurity Professionals: Those looking to enhance their skills in proactive defense and AI-driven threat detection.
Ethical Hackers: Individuals focused on mastering advanced hacking techniques and staying ahead of emerging cybersecurity threats.
Technology Leaders and Decision Makers: Executives and managers aiming to understand how AI and ethical hacking can secure their organizations.
Aspiring Students: Learners interested in building a career in cybersecurity, gaining foundational knowledge and practical skills in ethical hacking.
Stay Ahead of Technological AdvancementsValidates capability in AI is transforming cybersecurity, enabling you to stay at the forefront of evolving threats.
Bridge the Gap Between AI and CybersecurityValidates competency in combining AI techniques with ethical hacking to improve digital defense strategies.
Boost Career OpportunitiesExam includes scenario-based evaluation of AI-driven security methods, validating competency in addressing real-world cyber threats.
Future-Proof Your SkillsValidates expertise in AI-powered ethical hacking, positioning you as an expert in a rapidly advancing digital landscape.
Module 1: Cybersecurity, Ethical Hacking, and AI Foundations
1.1 Cybersecurity Fundamentals
1.2 Ethical Hacking Fundamentals
1.3 Core Security Frameworks
1.4 AI in Cybersecurity
1.5 Safe and Responsible AI Usage
Module 2: Networking, Linux, Web Technologies, and Reconnaissance Fundamentals
2.1 Networking Fundamentals
2.2 Linux Fundamentals for Ethical Hacking
2.3 Web Application Fundamentals
2.4 Reconnaissance Fundamentals
2.5 AI-Assisted Reconnaissance
Module 3: AI-Enhanced Reconnaissance, Enumeration, and Attack Surface Mapping
3.1 Reconnaissance Methodology
3.2 Enumeration Fundamentals
3.3 Cloud Reconnaissance
3.4 API Reconnaissance
3.5 AI for Attack Surface Mapping
Module 4: Vulnerability Assessment and Risk Prioritization
4.1 Vulnerability Assessment Basics
4.2 Vulnerability Scanning
4.3 Vulnerability Validation
4.4 AI-Assisted Vulnerability Analysis
4.5 Remediation Planning
Module 5: AI-Assisted Penetration Testing: Web, API, Password, Cloud, and AI Application Security
5.1 Penetration Testing Lifecycle
5.2 Web Application Security
5.3 API Security Testing Fundamentals
5.4 Password Security
5.5 Cloud and Container Pentesting Concepts
5.6 AI in Penetration Testing
Module 6: LLM, RAG, and AI Application Security
6.1 LLM Application Architecture
6.2 OWASP LLM Top 10
6.3 Prompt Injection Fundamentals
6.4 RAG Security (Retrieval-Augmented Generation)
6.5 AI Agent Security
6.6 Defensive Controls
Module 7: Machine Learning for Cybersecurity and Adversarial AI
7.1 Machine Learning Fundamentals
7.2 Supervised Learning for Security
7.3 Unsupervised Learning for Security
7.4 Natural Language Processing (NLP) for Cybersecurity
7.5 Adversarial Machine Learning
7.6 Explainable AI (XAI)
Module 8: Threat Hunting, Detection Engineering, and AI-Driven SOC Operations
8.1 Security Operations Center (SOC)
8.2 Threat Hunting Fundamentals
8.3 Detection Engineering
8.4 AI in the Security Operations Center (SOC)
8.5 Threat Intelligence
Module 9: Incident Response, Digital Forensics, and AI-Assisted Investigations
9.1 Incident Response Lifecycle
9.2 Incident Classification
9.3 Evidence Handling
9.4 Digital Forensics Basics
9.5 AI-Assisted Incident Response
9.6 AI-Specific Incident Response
Module 10: Identity Security, Zero Trust, Cloud Security, and Non-Human Identity
10.1 Identity and Access Management (IAM) Basics
10.2 Zero Trust Fundamentals
10.3 Privileged Access Management (PAM)
10.4 Non-Human Identity Security
10.5 Cloud Security Fundamentals
10.6 AI Agent Identity Controls
Module 11: AI Governance, Compliance, Risk Management, and Responsible Disclosure
11.1 AI Governance Fundamentals
11.2 NIST AI RMF and Generative AI Profile
11.3 Privacy and Compliance
11.4 AI Ethics
11.5 Responsible Disclosure
11.6 AI Security Reporting
Module 12: Capstone Project - End-to-End AI-Assisted Ethical Hacking and Defense Engagement
12.1 Scoping and Rules of Engagement
12.2 Reconnaissance and OSINT
12.3 Vulnerability Assessment
12.4 AI-Assisted Penetration Testing
12.5 LLM and AI Application Security Testing
12.6 Detection Engineering and Threat Hunting
12.7 Incident Response
12.8 Governance and Reporting
Optional Module: AI Agents for Ethical Hacking
1.1 What Are AI Agents?
1.2 Applications and Trends of AI Agents
1.3 The Importance of AI Agents in Ethical Hacking
1.4 How Does an AI Agent Work?
1.5 Characteristics of AI Agents in Ethical Hacking
1.6 Types of AI Agents
Tools you will explore
Acunetix
Wapiti
Nessus
OWASP ZAP
HackerGPT
Cobalt Strike
Shodan
Wazuh
Sumo Logic
YARA Rules
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+ Security Compliance Practitioner™
Nieuwegein
ma 25 jan. 2027
Empowering Compliance Through AI
The AI+ Security Compliance Practitioner™ is an advanced certification that merges the fundamental principles of cybersecurity compliance with the transformative power of artificial intelligence (AI). Building on the CISSP framework, this certification focuses on how AI can enhance compliance processes, improve risk management, and ensure robust security measures in alignment with regulatory standards. This certification validates knowledge of core principles of cyber security compliance, and assesses understanding of how AI can enhance security posture. This certification structure integrates comprehensive cybersecurity compliance principles with advanced AI applications, validating candidate competency in ensuring compliance and enhancing security through AI technologies.
Cybersecurity Professionals: Enhance skills in compliance, security management, and AI-driven security practices.
Risk Management Specialists: Learn AI-based approaches for risk assessment and mitigation.
Compliance Officers: Apply AI to improve regulatory compliance processes and security governance.
IT Security Analysts: Integrate AI technologies into security frameworks and operations.
Comprehensive Cybersecurity KnowledgeValidates comprehensive knowledge of cybersecurity compliance, aligned with the CISSP framework, to demonstrate competency in safeguarding organizations.
AI-Powered Risk ManagementValidates capability in AI can enhance risk management practices, offering smarter solutions to mitigate security threats.
Regulatory Compliance IntegrationUnderstand how to align AI-driven security measures with evolving regulatory standards, ensuring legal compliance.
Advanced Security PostureDemonstrates competency in enhancing your organization's security posture by applying AI technologies to strengthen defense mechanisms.
In-Demand ExpertiseValidates your competency with cutting-edge AI and cybersecurity compliance knowledge, positioning you as a leader in the industry.
Module 1: Foundations of AI-Enabled Security Compliance
1.1 Fundamentals of Security Compliance
1.2 Fundamentals of AI for Compliance Professionals
1.3 Applications of AI Across the Compliance Lifecycle
1.4 Case Study: Manual Compliance Process Causing Audit Delays
1.5 Use Case: AI Compliance Assistant for a Growing Organization
Module 2: Using AI to Interpret Security Standards and Requirements
2.1 Understanding Security Frameworks and Standards
2.2 AI-Assisted Requirement Interpretation
2.3 AI-Enabled Control Mapping
2.4 Case Study: Multiple Framework Compliance at a Technology Company
2.5 Use Case: AI-Based Cross-Framework Control Mapping
Module 3: AI-Assisted Security Risk and Compliance Gap Assessment
3.1 Security Risk Assessment Fundamentals
3.2 Using AI for Security Risk Identification
3.3 AI-Driven Compliance Gap Assessment
3.4 Case Study: Compliance Gaps Following a Security Assessment
3.5 Use Case: AI-Assisted Security Risk Register
Module 4: AI for Security Policy and Procedure Management
4.1 Security Governance Documentation
4.2 Using AI to Create and Improve Security Policies
4.3 AI for Policy Compliance Monitoring
4.4 Case Study: Outdated Information Security Policies
4.5 Use Case: AI Policy Review Assistant
Module 5: AI for Control Implementation and Evidence Management
5.1 Security Control Implementation
5.2 AI-Assisted Control Design and Documentation
5.3 AI-Enabled Evidence Management
5.4 Case Study: Incomplete Evidence During a Compliance Audit
5.5 Use Case: AI-Based Compliance Evidence Reviewer
Module 6: AI for Third-Party Security Compliance
6.1 Third-Party Security Risk
6.2 AI-Assisted Vendor Security Assessment
6.3 AI for Continuous Third-Party Monitoring
6.4 Case Study: Critical Cloud Vendor with Security Deficiencies
6.5 Use Case: AI Vendor Security Review Assistant
Module 7: AI for Compliance Monitoring, Incident Management, and Auditing
7.1 AI-Enabled Compliance Monitoring
7.2 AI for Security Incident Compliance
7.3 AI-Assisted Security Auditing
7.4 Case Study: Repeated Control Failures Before an Audit
7.5 Use Case: AI Security Compliance Monitoring Dashboard
Module 8: AI-Powered Security Compliance Program and Capstone
8.1 Establishing an AI-Enabled Compliance Program
8.2 Governance of AI Used for Compliance
8.3 AI Agents for Compliance Automation
8.4 Case Study: Organization-Wide AI Compliance Transformation
8.5 Use Case: AI-Powered Security Compliance Control Tower
8.6 Capstone Hands-on: Build an End-to-End AI-Powered Security Compliance Program
Optional Module: AI Agents Security Compliance
1.1 What Are AI Agents?
1.2 Key Capabilities of AI Agents in Cyber Security Compliance
1.3 Applications and Trends for AI Agents in Security Compliance
1.4 How Does an AI Agent Work?
1.5 Core Characteristics of AI Agents
1.6 Types of AI Agents
Tools you will explore
Checkmarx
Snyk
Nmap
Wireshark
Burp Suite
Nessus
Splunk
IBM QRadar
Palo Alto Cortex XSOAR
Microsoft Sentinel
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
Flexible Online Learning with a One-Year Subscription
Get a complete learning experience with one year of subscription access, including course updates and these features:
Learn Across Devices
Access learning materials on your tablet or mobile phone.
Exam Preparation Resources
Prepare with a comprehensive exam study guide.
Online Proctored Exam
Take your certification exam with one free retake.
Quizzes and Assessments
Reinforce your knowledge with practice assessments and course resources.
AI-Powered Learning Support
Get personalized guidance from the AI Mentor.
E-Books and Podcasts
Access study materials in PDF and audio formats, along with podcasts.
On-Demand Video Lessons
Learn through high-quality educational videos.
€3.930
Klassikaal
max 12
1 dag
AI+ Security Compliance Practitioner™ eLearning
Empowering Compliance Through AI
The AI+ Security Compliance Practitioner™ is an advanced certification that merges the fundamental principles of cybersecurity compliance with the transformative power of artificial intelligence (AI). Building on the CISSP framework, this certification focuses on how AI can enhance compliance processes, improve risk management, and ensure robust security measures in alignment with regulatory standards. This certification validates knowledge of core principles of cyber security compliance, and assesses understanding of how AI can enhance security posture. This certification structure integrates comprehensive cybersecurity compliance principles with advanced AI applications, validating candidate competency in ensuring compliance and enhancing security through AI technologies.
Cybersecurity Professionals: Enhance skills in compliance, security management, and AI-driven security practices.
Risk Management Specialists: Learn AI-based approaches for risk assessment and mitigation.
Compliance Officers: Apply AI to improve regulatory compliance processes and security governance.
IT Security Analysts: Integrate AI technologies into security frameworks and operations.
Comprehensive Cybersecurity KnowledgeValidates comprehensive knowledge of cybersecurity compliance, aligned with the CISSP framework, to demonstrate competency in safeguarding organizations.
AI-Powered Risk ManagementValidates capability in AI can enhance risk management practices, offering smarter solutions to mitigate security threats.
Regulatory Compliance IntegrationUnderstand how to align AI-driven security measures with evolving regulatory standards, ensuring legal compliance.
Advanced Security PostureDemonstrates competency in enhancing your organization's security posture by applying AI technologies to strengthen defense mechanisms.
In-Demand ExpertiseValidates your competency with cutting-edge AI and cybersecurity compliance knowledge, positioning you as a leader in the industry.
Module 1: Foundations of AI-Enabled Security Compliance
1.1 Fundamentals of Security Compliance
1.2 Fundamentals of AI for Compliance Professionals
1.3 Applications of AI Across the Compliance Lifecycle
1.4 Case Study: Manual Compliance Process Causing Audit Delays
1.5 Use Case: AI Compliance Assistant for a Growing Organization
Module 2: Using AI to Interpret Security Standards and Requirements
2.1 Understanding Security Frameworks and Standards
2.2 AI-Assisted Requirement Interpretation
2.3 AI-Enabled Control Mapping
2.4 Case Study: Multiple Framework Compliance at a Technology Company
2.5 Use Case: AI-Based Cross-Framework Control Mapping
Module 3: AI-Assisted Security Risk and Compliance Gap Assessment
3.1 Security Risk Assessment Fundamentals
3.2 Using AI for Security Risk Identification
3.3 AI-Driven Compliance Gap Assessment
3.4 Case Study: Compliance Gaps Following a Security Assessment
3.5 Use Case: AI-Assisted Security Risk Register
Module 4: AI for Security Policy and Procedure Management
4.1 Security Governance Documentation
4.2 Using AI to Create and Improve Security Policies
4.3 AI for Policy Compliance Monitoring
4.4 Case Study: Outdated Information Security Policies
4.5 Use Case: AI Policy Review Assistant
Module 5: AI for Control Implementation and Evidence Management
5.1 Security Control Implementation
5.2 AI-Assisted Control Design and Documentation
5.3 AI-Enabled Evidence Management
5.4 Case Study: Incomplete Evidence During a Compliance Audit
5.5 Use Case: AI-Based Compliance Evidence Reviewer
Module 6: AI for Third-Party Security Compliance
6.1 Third-Party Security Risk
6.2 AI-Assisted Vendor Security Assessment
6.3 AI for Continuous Third-Party Monitoring
6.4 Case Study: Critical Cloud Vendor with Security Deficiencies
6.5 Use Case: AI Vendor Security Review Assistant
Module 7: AI for Compliance Monitoring, Incident Management, and Auditing
7.1 AI-Enabled Compliance Monitoring
7.2 AI for Security Incident Compliance
7.3 AI-Assisted Security Auditing
7.4 Case Study: Repeated Control Failures Before an Audit
7.5 Use Case: AI Security Compliance Monitoring Dashboard
Module 8: AI-Powered Security Compliance Program and Capstone
8.1 Establishing an AI-Enabled Compliance Program
8.2 Governance of AI Used for Compliance
8.3 AI Agents for Compliance Automation
8.4 Case Study: Organization-Wide AI Compliance Transformation
8.5 Use Case: AI-Powered Security Compliance Control Tower
8.6 Capstone Hands-on: Build an End-to-End AI-Powered Security Compliance Program
Optional Module: AI Agents Security Compliance
1.1 What Are AI Agents?
1.2 Key Capabilities of AI Agents in Cyber Security Compliance
1.3 Applications and Trends for AI Agents in Security Compliance
1.4 How Does an AI Agent Work?
1.5 Core Characteristics of AI Agents
1.6 Types of AI Agents
Tools you will explore
Checkmarx
Snyk
Nmap
Wireshark
Burp Suite
Nessus
Splunk
IBM QRadar
Palo Alto Cortex XSOAR
Microsoft Sentinel
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
Flexible Online Learning with a One-Year Subscription
Get a complete learning experience with one year of subscription access, including course updates and these features:
Learn Across Devices
Access learning materials on your tablet or mobile phone.
Exam Preparation Resources
Prepare with a comprehensive exam study guide.
Online Proctored Exam
Take your certification exam with one free retake.
Quizzes and Assessments
Reinforce your knowledge with practice assessments and course resources.
AI-Powered Learning Support
Get personalized guidance from the AI Mentor.
E-Books and Podcasts
Access study materials in PDF and audio formats, along with podcasts.
On-Demand Video Lessons
Learn through high-quality educational videos.
€530
E-Learning
max 999
1 dag
AI+ Telecommunications Practitioner™
Formerly known as AI+ Telecommunications™
AI in Telecommunications: Redefining the Future of Seamless Connectivity
Foundational Insights: Explore AI technologies enhancing telecom networks, from predictive maintenance to network optimization and customer service automation.
Advanced Applications: Master AI in 5G deployment, anomaly detection, and real-time resource management for improved network performance.
Specialized Expertise: Learn AI solutions for cybersecurity, fraud detection, and efficient IoT integration to ensure network reliability.
Capstone Project: Develop AI-driven solutions for real-world telecom challenges like network optimization and intelligent service delivery.
Telecom Engineers: Professionals looking to integrate AI for network optimization, 5G deployment, and predictive maintenance.
Data Analysts: Individuals eager to apply AI in data processing and analysis within the telecom industry.
Network Security Experts: Those interested in leveraging AI to enhance telecom infrastructure security and threat detection.
AI Enthusiasts: People with a passion for AI looking to apply it in the rapidly evolving telecommunications sector.
Project Managers: Professionals overseeing telecom projects who wish to understand AI’s impact on efficiency and innovation in telecom.
AI-Powered Telecom InnovationLearn to integrate AI technologies to enhance telecom services, from 5G to cybersecurity.
Practical, Hands-On LearningEngage in real-world projects, simulating AI applications across various telecom domains.
Cutting-Edge Industry RelevanceStay ahead of emerging trends like AI-driven network optimization and IoT integration.
Comprehensive Skill DevelopmentGain expertise in data engineering, AI algorithms, and predictive maintenance for telecom infrastructure.
Ethical & Strategic InsightsExplore ethical considerations in AI deployment and its impact on the telecom industry.
Module 1: Introduction to AI in Telecommunications
1.1 AI Fundamentals in Telecommunications
1.2 AI Technologies for Telecom
1.3 Emerging Trends in AI for Telecommunications
1.4 Case Study
1.5 Hands-on
Module 2: Data Engineering for Telecom AI
2.1 Foundation of Telecom Data Engineering
2.2 Designing and Managing the Telecom Data Pipeline
2.3 Data Engineering tools and Technology
2.4 Case Study: SK Telecom’s Big Data Analytics with Metatron Discovery
2.5 Hands on Exercise
Module 3: AI for 5G Networks
3.1 Introduction to 5G
3.2 AI Applications in 5G
3.3 Enhancing Network Management with AI
3.4 Case Study
3.5 Hands-on
Module 4: AI in Network Optimization
4.1 Predictive Network Management
4.2 Performance Enhancement Techniques
4.3 Traffic Management Strategies
4.4 Case Study
4.5 Hands-on
Module 5: AI in Network Security
5.1 Security Threats in Telecom
5.2 AI Security Solutions
5.3 Advanced Security Frameworks
5.4 Case Study
5.5 Hands-on
Module 6: Enhancing Customer Experience with AI
6.1 Personalized Customer Service
6.2 Service Quality Improvement
6.3 Enhancing Customer Engagement
6.4 Case Study
6.5 Hands-on
Module 7: IoT Integration with Telecommunications
7.1 IoT Fundamentals
7.2 Managing IoT Security Challenges
7.3 Enhancing Operational Efficiency with IoT
7.4 Case Study
7.5 Hands-on
Module 8: AI-Integrated Network Operations Centers (NOC)
8.1 Transitioning to AI-driven NOCs
8.2 Automating escalations and root cause analyses
8.3 Closed-loop automation with AI and SDN integration
8.4 Designing AI-ready network architectures
8.5 Change management strategies for AI rollouts in operations
8.6 Case Study: Implementation of AI assistants in NOCs
Module 9: Ethical Considerations in Artificial Intelligence
9.1 Ethical Implications of Using Artificial Intelligence
9.2 Responsible Deployment Practices
9.3 Emerging Trends and Challenges
9.4 Case Study
9.5 Hands-on
Module 10: Capstone Project Tools you will explore
TensorFlow
Keras
Matplotlib
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+ Telecommunications Practitioner™ eLearning
Formerly known as AI+ Telecommunications™
AI in Telecommunications: Redefining the Future of Seamless Connectivity
Foundational Insights: Explore AI technologies enhancing telecom networks, from predictive maintenance to network optimization and customer service automation.
Advanced Applications: Master AI in 5G deployment, anomaly detection, and real-time resource management for improved network performance.
Specialized Expertise: Learn AI solutions for cybersecurity, fraud detection, and efficient IoT integration to ensure network reliability.
Capstone Project: Develop AI-driven solutions for real-world telecom challenges like network optimization and intelligent service delivery.
Telecom Engineers: Professionals looking to integrate AI for network optimization, 5G deployment, and predictive maintenance.
Data Analysts: Individuals eager to apply AI in data processing and analysis within the telecom industry.
Network Security Experts: Those interested in leveraging AI to enhance telecom infrastructure security and threat detection.
AI Enthusiasts: People with a passion for AI looking to apply it in the rapidly evolving telecommunications sector.
Project Managers: Professionals overseeing telecom projects who wish to understand AI’s impact on efficiency and innovation in telecom.
AI-Powered Telecom InnovationLearn to integrate AI technologies to enhance telecom services, from 5G to cybersecurity.
Practical, Hands-On LearningEngage in real-world projects, simulating AI applications across various telecom domains.
Cutting-Edge Industry RelevanceStay ahead of emerging trends like AI-driven network optimization and IoT integration.
Comprehensive Skill DevelopmentGain expertise in data engineering, AI algorithms, and predictive maintenance for telecom infrastructure.
Ethical & Strategic InsightsExplore ethical considerations in AI deployment and its impact on the telecom industry.
Module 1: Introduction to AI in Telecommunications
1.1 AI Fundamentals in Telecommunications
1.2 AI Technologies for Telecom
1.3 Emerging Trends in AI for Telecommunications
1.4 Case Study
1.5 Hands-on
Module 2: Data Engineering for Telecom AI
2.1 Foundation of Telecom Data Engineering
2.2 Designing and Managing the Telecom Data Pipeline
2.3 Data Engineering tools and Technology
2.4 Case Study: SK Telecom’s Big Data Analytics with Metatron Discovery
2.5 Hands on Exercise
Module 3: AI for 5G Networks
3.1 Introduction to 5G
3.2 AI Applications in 5G
3.3 Enhancing Network Management with AI
3.4 Case Study
3.5 Hands-on
Module 4: AI in Network Optimization
4.1 Predictive Network Management
4.2 Performance Enhancement Techniques
4.3 Traffic Management Strategies
4.4 Case Study
4.5 Hands-on
Module 5: AI in Network Security
5.1 Security Threats in Telecom
5.2 AI Security Solutions
5.3 Advanced Security Frameworks
5.4 Case Study
5.5 Hands-on
Module 6: Enhancing Customer Experience with AI
6.1 Personalized Customer Service
6.2 Service Quality Improvement
6.3 Enhancing Customer Engagement
6.4 Case Study
6.5 Hands-on
Module 7: IoT Integration with Telecommunications
7.1 IoT Fundamentals
7.2 Managing IoT Security Challenges
7.3 Enhancing Operational Efficiency with IoT
7.4 Case Study
7.5 Hands-on
Module 8: AI-Integrated Network Operations Centers (NOC)
8.1 Transitioning to AI-driven NOCs
8.2 Automating escalations and root cause analyses
8.3 Closed-loop automation with AI and SDN integration
8.4 Designing AI-ready network architectures
8.5 Change management strategies for AI rollouts in operations
8.6 Case Study: Implementation of AI assistants in NOCs
Module 9: Ethical Considerations in Artificial Intelligence
9.1 Ethical Implications of Using Artificial Intelligence
9.2 Responsible Deployment Practices
9.3 Emerging Trends and Challenges
9.4 Case Study
9.5 Hands-on
Module 10: Capstone Project Tools you will explore
TensorFlow
Keras
Matplotlib
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+ Developer Practitioner™
Nieuwegein
ma 7 dec. 2026
Formerly known as AI+ Developer™
Get hands-on with the tools and technologies that power the AI ecosystem.
Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
Advanced Modules: Includes time series, model explainability, and cloud deployment
Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
Master Key AI Development Skills:Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
Specialize in Cutting-Edge AI Domains:Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
Stay Ahead in AI Development:AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.
Advance Your Career in AI Development:With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.
Module 1: Foundations of Modern AI for Developers
1.1 Understanding Artificial Intelligence
1.2 Components of an AI Application
1.3 Beginner AI Development Workflow
1.4 AI Development Concepts and Limitations
1.5 Case Study: A Chatbot Prototype That Produced Unreliable Answers
1.6 Use Case: Selecting the Right AI Approach
Module 2: Python Programming for AI
2.1 Python Foundations
2.2 Python Data Structures and File Handling
2.3 Beginner Software-Development Practices
2.4 Case Study: An Unstructured Python Script Becomes Difficult to Maintain
2.5 Use Case: Automated File Processing Utility
Module 3: Data Handling and Visualization
3.1 Working with NumPy and Pandas
3.2 Data Cleaning
3.3 Exploratory Data Analysis
3.4 Case Study: Dirty Customer Data Produces Incorrect Sales Insights
3.5 Use Case: Retail Sales Data Preparation
Module 4: Practical Mathematics and Statistics for AI
4.1 Essential Mathematical Concepts
4.2 Essential Statistics
4.3 Mathematical Reasoning for AI
4.4 Case Study: Average Performance Hides a Major Customer Problem
4.5 Use Case: Similarity-Based Product Recommendation
Module 5: Machine Learning Fundamentals
5.1 Understanding Machine Learning
5.2 Supervised Machine Learning
5.3 Unsupervised and Other Beginner Methods
5.4 Case Study: Customer Churn Prediction
5.5 Use Case: Delivery-Time Prediction
Module 6: Model Evaluation and Improvement
6.1 Model Evaluation Metrics
6.2 Improving Model Performance
6.3 Reliable Model Delivery
6.4 Case Study: A High-Accuracy Model Misses the Important Cases
6.5 Use Case: Spam Email Detection
Module 7: Deep Learning and Computer Vision Basics
7.1 Neural Network Fundamentals
7.2 Beginner Deep Learning with PyTorch
7.3 Computer Vision Foundations
7.4 Case Study: Manufacturing Defect Detection with Transfer Learning
7.5 Use Case: Product Image Classification
Module 8: Natural Language Processing, Transformers, and LLM Fundamentals
8.1 Text Processing Fundamentals
8.2 Embeddings and Transformers
8.3 Large Language Model Fundamentals
8.4 Case Study: Choosing Between a Text Classifier and an LLM for Routing Support Tickets
8.5 Use Case: Customer Review Analysis
Module 9: Generative and Multimodal AI Application Development
9.1 Prompt Engineering Foundations
9.2 Building Controlled Generative AI Applications
9.3 Multimodal AI Foundations
9.4 Case Study: Invoice Extraction Produces Incorrect Financial Fields
9.5 Use Case: Multimodal Product Information Assistant
Module 10: Retrieval-Augmented Generation and Knowledge Assistants
10.1 Retrieval Fundamentals
10.2 Building a Basic RAG Workflow
10.3 RAG Quality and Control
10.4 Case Study: A Policy Assistant Returns an Outdated Rule
10.5 Use Case: Employee Handbook Assistant
Module 11: Simple AI Agents, APIs, and Deployment
11.1 API Development for AI
11.2 Basic AI Agents and Tool Use
11.3 Beginner Deployment and Operations
11.4 Case Study: An Over-Privileged Agent Performs an Unapproved Action
11.5 Use Case: IT Support Triage Assistant
Module 12: Responsible AI, Security, Monitoring, and Capstone
12.1 Responsible AI Foundations
12.2 AI Application Security
12.3 Monitoring and Production Readiness
12.4 Case Study: Prompt Injection Causes Confidential Data Exposure
12.5 Use Case: Beginner AI Release Checklist
Optional Module: AI Agents for Developer
1.1 What Are AI Agents?
1.2 Significance of AI Agents for Developers
1.3 Applications and Trends of AI Agents for Developers
1.4 How Does an AI Agent Work?
1.5 Core Characteristics of AI Agents
1.6 Importance of AI Agents
1.7 Types of AI Agents
1.8 Comparison Table of AI Agents in Ethics
Tools you will explore
GitHub Copilot
Lobe
H2O.ai
Snorkel
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+ Developer Practitioner™ eLearning
Formerly known as AI+ Developer™
Get hands-on with the tools and technologies that power the AI ecosystem.
Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
Advanced Modules: Includes time series, model explainability, and cloud deployment
Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
Master Key AI Development Skills:Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
Specialize in Cutting-Edge AI Domains:Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
Stay Ahead in AI Development:AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.
Advance Your Career in AI Development:With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.
Module 1: Foundations of Modern AI for Developers
1.1 Understanding Artificial Intelligence
1.2 Components of an AI Application
1.3 Beginner AI Development Workflow
1.4 AI Development Concepts and Limitations
1.5 Case Study: A Chatbot Prototype That Produced Unreliable Answers
1.6 Use Case: Selecting the Right AI Approach
Module 2: Python Programming for AI
2.1 Python Foundations
2.2 Python Data Structures and File Handling
2.3 Beginner Software-Development Practices
2.4 Case Study: An Unstructured Python Script Becomes Difficult to Maintain
2.5 Use Case: Automated File Processing Utility
Module 3: Data Handling and Visualization
3.1 Working with NumPy and Pandas
3.2 Data Cleaning
3.3 Exploratory Data Analysis
3.4 Case Study: Dirty Customer Data Produces Incorrect Sales Insights
3.5 Use Case: Retail Sales Data Preparation
Module 4: Practical Mathematics and Statistics for AI
4.1 Essential Mathematical Concepts
4.2 Essential Statistics
4.3 Mathematical Reasoning for AI
4.4 Case Study: Average Performance Hides a Major Customer Problem
4.5 Use Case: Similarity-Based Product Recommendation
Module 5: Machine Learning Fundamentals
5.1 Understanding Machine Learning
5.2 Supervised Machine Learning
5.3 Unsupervised and Other Beginner Methods
5.4 Case Study: Customer Churn Prediction
5.5 Use Case: Delivery-Time Prediction
Module 6: Model Evaluation and Improvement
6.1 Model Evaluation Metrics
6.2 Improving Model Performance
6.3 Reliable Model Delivery
6.4 Case Study: A High-Accuracy Model Misses the Important Cases
6.5 Use Case: Spam Email Detection
Module 7: Deep Learning and Computer Vision Basics
7.1 Neural Network Fundamentals
7.2 Beginner Deep Learning with PyTorch
7.3 Computer Vision Foundations
7.4 Case Study: Manufacturing Defect Detection with Transfer Learning
7.5 Use Case: Product Image Classification
Module 8: Natural Language Processing, Transformers, and LLM Fundamentals
8.1 Text Processing Fundamentals
8.2 Embeddings and Transformers
8.3 Large Language Model Fundamentals
8.4 Case Study: Choosing Between a Text Classifier and an LLM for Routing Support Tickets
8.5 Use Case: Customer Review Analysis
Module 9: Generative and Multimodal AI Application Development
9.1 Prompt Engineering Foundations
9.2 Building Controlled Generative AI Applications
9.3 Multimodal AI Foundations
9.4 Case Study: Invoice Extraction Produces Incorrect Financial Fields
9.5 Use Case: Multimodal Product Information Assistant
Module 10: Retrieval-Augmented Generation and Knowledge Assistants
10.1 Retrieval Fundamentals
10.2 Building a Basic RAG Workflow
10.3 RAG Quality and Control
10.4 Case Study: A Policy Assistant Returns an Outdated Rule
10.5 Use Case: Employee Handbook Assistant
Module 11: Simple AI Agents, APIs, and Deployment
11.1 API Development for AI
11.2 Basic AI Agents and Tool Use
11.3 Beginner Deployment and Operations
11.4 Case Study: An Over-Privileged Agent Performs an Unapproved Action
11.5 Use Case: IT Support Triage Assistant
Module 12: Responsible AI, Security, Monitoring, and Capstone
12.1 Responsible AI Foundations
12.2 AI Application Security
12.3 Monitoring and Production Readiness
12.4 Case Study: Prompt Injection Causes Confidential Data Exposure
12.5 Use Case: Beginner AI Release Checklist
Optional Module: AI Agents for Developer
1.1 What Are AI Agents?
1.2 Significance of AI Agents for Developers
1.3 Applications and Trends of AI Agents for Developers
1.4 How Does an AI Agent Work?
1.5 Core Characteristics of AI Agents
1.6 Importance of AI Agents
1.7 Types of AI Agents
1.8 Comparison Table of AI Agents in Ethics
Tools you will explore
GitHub Copilot
Lobe
H2O.ai
Snorkel
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+ Architect Practitioner™
Formerly known as AI+ Architect™
Visualize Tomorrow: Neural Networks in Vision
Deep AI Expertise: Covers neural networks, NLP, and computer vision frameworks
Enterprise AI: Learn to design scalable AI systems for real-world impact
Capstone Integration: Build, test, and deploy advanced AI architectures
Industry Preparedness: Equips you for roles in high-demand AI design domains
Architecture Professionals: Enhance your architectural design skills by integrating AI to create scalable, efficient, and intelligent systems for modern solutions.
Systems Architects & Engineers: Learn to leverage AI to design and build sophisticated, scalable infrastructures while automating key processes.
IT Infrastructure Managers: Use AI to optimize architecture planning, streamline infrastructure deployment, and ensure seamless system integration.
Business Leaders: Drive transformation within your organization by adopting AI-driven architectural solutions to enhance scalability, reduce costs.
Students & New Graduates: Gain a competitive edge in the tech industry by mastering AI architectural techniques and tools.
Leverage AI for Smarter Architecture Decisions:Learn how to use AI tools to optimize architectural design, improve scalability.
Enhance AI Integration in Architectural Projects:Use AI to integrate innovative solutions into your architectural designs, automating workflows.
Stay Ahead in AI-Powered Architecture Innovation:As AI adoption in architecture accelerates, professionals with advanced AI knowledge.
Boost Strategic Decision-Making with AI Insights:Master AI models to analyze architectural data, predict trends, and drive data-driven decisions.
Advance Your Career in AI Architecture:As AI revolutionizes architecture, this certification equips you with the skills to lead AI initiatives.
Certification Overview
Course Introduction Preview
Module 1: Fundamentals of Neural Networks
1.1 Introduction to Neural Networks
1.2 Neural Network Architecture
1.3 Hands-on: Implement a Basic Neural Network
Module 2: Neural Network Optimization
2.1 Hyperparameter Tuning
2.2 Optimization Algorithms
2.3 Regularization Techniques
2.4 Hands-on: Hyperparameter Tuning and Optimization
Module 3: Neural Network Architectures for NLP
3.1 Key NLP Concepts
3.2 NLP-Specific Architectures
3.3 Hands-on: Implementing an NLP Model
Module 4: Neural Network Architectures for Computer Vision
4.1 Key Computer Vision Concepts
4.2 Computer Vision-Specific Architectures
4.3 Hands-on: Building a Computer Vision Model
Module 5: Model Evaluation and Performance Metrics
5.1 Model Evaluation Techniques
5.2 Improving Model Performance
5.3 Hands-on: Evaluating and Optimizing AI Models
Module 6: AI Infrastructure and Deployment
6.1 Infrastructure for AI Development
6.2 Deployment Strategies
6.3 Hands-on: Deploying an AI Model
Module 7: AI Ethics and Responsible AI Design
7.1 Ethical Considerations in AI
7.2 Best Practices for Responsible AI Design
7.3 Hands-on: Analyzing Ethical Considerations in AI
Module 8: Generative AI Models
8.1 Overview of Generative AI Models
8.2 Generative AI Applications in Various Domains
8.3 Hands-on: Exploring Generative AI Models
Module 9: Research-Based AI Design
9.1 AI Research Techniques
9.2 Cutting-Edge AI Design
9.3 Hands-on: Analyzing AI Research Papers
Module 10: Capstone Project and Course Review
10.1 Capstone Project Presentation
10.2 Course Review and Future Directions
10.3 Hands-on: Capstone Project Development
Optional Module: AI Agents for Architect
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
AutoGluon
ChatGPT
SonarCube
Vertex 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
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+ Architect Practitioner™ eLearning
Formerly known as AI+ Architect™
Visualize Tomorrow: Neural Networks in Vision
Deep AI Expertise: Covers neural networks, NLP, and computer vision frameworks
Enterprise AI: Learn to design scalable AI systems for real-world impact
Capstone Integration: Build, test, and deploy advanced AI architectures
Industry Preparedness: Equips you for roles in high-demand AI design domains
Architecture Professionals: Enhance your architectural design skills by integrating AI to create scalable, efficient, and intelligent systems for modern solutions.
Systems Architects & Engineers: Learn to leverage AI to design and build sophisticated, scalable infrastructures while automating key processes.
IT Infrastructure Managers: Use AI to optimize architecture planning, streamline infrastructure deployment, and ensure seamless system integration.
Business Leaders: Drive transformation within your organization by adopting AI-driven architectural solutions to enhance scalability, reduce costs.
Students & New Graduates: Gain a competitive edge in the tech industry by mastering AI architectural techniques and tools.
Leverage AI for Smarter Architecture Decisions:Learn how to use AI tools to optimize architectural design, improve scalability.
Enhance AI Integration in Architectural Projects:Use AI to integrate innovative solutions into your architectural designs, automating workflows.
Stay Ahead in AI-Powered Architecture Innovation:As AI adoption in architecture accelerates, professionals with advanced AI knowledge.
Boost Strategic Decision-Making with AI Insights:Master AI models to analyze architectural data, predict trends, and drive data-driven decisions.
Advance Your Career in AI Architecture:As AI revolutionizes architecture, this certification equips you with the skills to lead AI initiatives.
Certification Overview
Course Introduction Preview
Module 1: Fundamentals of Neural Networks
1.1 Introduction to Neural Networks
1.2 Neural Network Architecture
1.3 Hands-on: Implement a Basic Neural Network
Module 2: Neural Network Optimization
2.1 Hyperparameter Tuning
2.2 Optimization Algorithms
2.3 Regularization Techniques
2.4 Hands-on: Hyperparameter Tuning and Optimization
Module 3: Neural Network Architectures for NLP
3.1 Key NLP Concepts
3.2 NLP-Specific Architectures
3.3 Hands-on: Implementing an NLP Model
Module 4: Neural Network Architectures for Computer Vision
4.1 Key Computer Vision Concepts
4.2 Computer Vision-Specific Architectures
4.3 Hands-on: Building a Computer Vision Model
Module 5: Model Evaluation and Performance Metrics
5.1 Model Evaluation Techniques
5.2 Improving Model Performance
5.3 Hands-on: Evaluating and Optimizing AI Models
Module 6: AI Infrastructure and Deployment
6.1 Infrastructure for AI Development
6.2 Deployment Strategies
6.3 Hands-on: Deploying an AI Model
Module 7: AI Ethics and Responsible AI Design
7.1 Ethical Considerations in AI
7.2 Best Practices for Responsible AI Design
7.3 Hands-on: Analyzing Ethical Considerations in AI
Module 8: Generative AI Models
8.1 Overview of Generative AI Models
8.2 Generative AI Applications in Various Domains
8.3 Hands-on: Exploring Generative AI Models
Module 9: Research-Based AI Design
9.1 AI Research Techniques
9.2 Cutting-Edge AI Design
9.3 Hands-on: Analyzing AI Research Papers
Module 10: Capstone Project and Course Review
10.1 Capstone Project Presentation
10.2 Course Review and Future Directions
10.3 Hands-on: Capstone Project Development
Optional Module: AI Agents for Architect
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
AutoGluon
ChatGPT
SonarCube
Vertex 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
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+ Engineer Practitioner™
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.
€3.930
Klassikaal
max 12
5 dagen