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68.894 resultaten

AI+ Security Compliance™

Nieuwegein ma 26 okt. 2026 en 1 andere data
Naleving bevorderen met behulp van AI De cursus AI+ Security Compliance™ is een geavanceerde cursus waarin de basisprincipes van cyberbeveiligingscompliance worden gecombineerd met de transformatieve kracht van kunstmatige intelligentie (AI). Voortbouwend op het CISSP-raamwerk richt deze cursus zich op hoe AI complianceprocessen kan versterken, risicobeheer kan verbeteren en robuuste beveiligingsmaatregelen kan waarborgen in overeenstemming met regelgevende normen. Deze cursus laat u kennismaken met de kernprincipes van cyberbeveiligingscompliance, terwijl het potentieel van AI wordt verkend om uw beveiligingspositie te versterken. De opzet van deze cursus integreert uitgebreide principes van cyberbeveiligingscompliance met geavanceerde AI-toepassingen, waardoor cursisten de nodige vaardigheden verwerven om compliance te waarborgen en de beveiliging te versterken door middel van AI-technologieën. Module 1: Inleiding tot naleving van cyberbeveiligingsvoorschriften en AI 1.1 Overzicht van naleving van cyberbeveiliging 1.2 Internationale nalevingsnormen 1.3 Complianceprogramma's ontwikkelen 1.4 Implementatie van nalevingsprogramma's 1.5 AI in naleving van cyberbeveiliging 1.6 Casestudy's en toepassingen Module 2: Beveiliging en risicobeheer met AI 2.1 Risicobeheerkaders 2.2 Risicobeoordelingen uitvoeren 2.3 AI bij risicobeoordeling 2.4 Naleving en AI 2.5 Incidentrespons en AI Module 3: Beveiliging van bedrijfsmiddelen en AI voor naleving 3.1 Gegevensclassificatie en -bescherming 3.2 AI in privacybescherming 3.3 Beheer van bedrijfsmiddelen met AI 3.4 Casestudy's en best practices Module 4: Beveiligingsarchitectuur en -engineering met AI 4.1 Principes van veilig ontwerp 4.2 AI in cryptografie 4.3 AI bij kwetsbaarheidsbeoordeling 4.4 Beveiligingsmodellen en AI Module 5: Communicatie- en netwerkbeveiliging met AI 5.1 Basisprincipes van netwerkbeveiliging 5.2 AI in netwerkmonitoring 5.3 AI-gestuurde netwerkbeveiliging 5.4 Compliance in netwerkbeveiliging Module 6: Identiteits- en toegangsbeheer (IAM) met AI 6.1 Basisprincipes van IAM 6.2 AI bij identiteitsverificatie 6.3 Toegangscontrole en AI 6.4 Bedreigingen voor IAM- en AI-oplossingen Module 7: Beveiligingsbeoordeling en incidentrespons met AI 7.1 Technieken voor beveiligingstesten 7.2 AI bij beveiligingstesten 7.3 Continue monitoring en AI 7.4 Planning van incidentrespons 7.5 Beheer van cyberbeveiligingsincidenten 7.6 Juridische en regelgevende overwegingen Module 8: Beveiligingsactiviteiten met AI 8.1 Security Operations Center (SOC) 8.2 Gegevensclassificatie en -bescherming 8.3 Naleving van privacywetgeving 8.4 Disaster Recovery en AI 8.5 AI in beveiligingscoördinatie Module 9: Beveiliging en audit van softwareontwikkeling met AI 9.1 Veilige softwareontwikkelingscyclus (SDLC) 9.2 AI bij het testen van applicatiebeveiliging 9.3 AI in veilige DevOps 9.4 Bedreigingsmodellering en AI 9.5 Interne en externe audits 9.6 Continue monitoring Module 10: Toekomstige trends in AI en naleving van cyberbeveiliging 10.1 Opkomende AI-technologieën 10.2 AI in cyberdreigingsinformatie 10.3 Kwantumcomputers en AI 10.4 Ethische overwegingen en AI-governance 10.5 Praktische toepassingen Optionele module: AI-agenten voor naleving van beveiligingsvoorschriften 1. Wat zijn AI-agenten 2. Belangrijkste mogelijkheden van AI-agenten bij cyberbeveiligingscompliance 3. Toepassingen en trends voor AI-agenten bij naleving van beveiligingsvoorschriften 4. Hoe werkt een AI-agent 5. Kernkenmerken van AI-agenten 6. Soorten AI-agenten Tools die u gaat verkennen Secureframe LeewayHertz Securiti Scytale Inclusief online examen onder toezicht, met één gratis herkansing. Examenopzet: 50 vragen, 70% vereist om te slagen, 90 minuten, online examen onder toezicht Toegang tot alle materialen en examens wordt gedurende 365 dagen na levering verleend. Cursus onder begeleiding van een docent OF cursus in eigen tempo + officieel examen + digitale badge
€3.930
Klassikaal
max 12
5 dagen

AI+ Security Compliance™ eLearning

Naleving bevorderen met behulp van AI De cursus AI+ Security Compliance™ is een geavanceerde cursus waarin de basisprincipes van cyberbeveiligingscompliance worden gecombineerd met de transformatieve kracht van kunstmatige intelligentie (AI). Voortbouwend op het CISSP-raamwerk richt deze cursus zich op hoe AI complianceprocessen kan versterken, risicobeheer kan verbeteren en robuuste beveiligingsmaatregelen kan waarborgen in overeenstemming met regelgevende normen. Deze cursus laat u kennismaken met de kernprincipes van cyberbeveiligingscompliance, terwijl het potentieel van AI wordt verkend om uw beveiligingspositie te versterken. De opzet van deze cursus integreert uitgebreide principes van cyberbeveiligingscompliance met geavanceerde AI-toepassingen, waardoor cursisten de nodige vaardigheden verwerven om compliance te waarborgen en de beveiliging te versterken door middel van AI-technologieën. Module 1: Inleiding tot naleving van cyberbeveiligingsvoorschriften en AI 1.1 Overzicht van naleving van cyberbeveiliging 1.2 Internationale nalevingsnormen 1.3 Complianceprogramma's ontwikkelen 1.4 Implementatie van nalevingsprogramma's 1.5 AI in naleving van cyberbeveiliging 1.6 Casestudy's en toepassingen Module 2: Beveiliging en risicobeheer met AI 2.1 Risicobeheerkaders 2.2 Risicobeoordelingen uitvoeren 2.3 AI bij risicobeoordeling 2.4 Naleving en AI 2.5 Incidentrespons en AI Module 3: Beveiliging van bedrijfsmiddelen en AI voor naleving 3.1 Gegevensclassificatie en -bescherming 3.2 AI in privacybescherming 3.3 Beheer van bedrijfsmiddelen met AI 3.4 Casestudy's en best practices Module 4: Beveiligingsarchitectuur en -engineering met AI 4.1 Principes van veilig ontwerp 4.2 AI in cryptografie 4.3 AI bij kwetsbaarheidsbeoordeling 4.4 Beveiligingsmodellen en AI Module 5: Communicatie- en netwerkbeveiliging met AI 5.1 Basisprincipes van netwerkbeveiliging 5.2 AI in netwerkmonitoring 5.3 AI-gestuurde netwerkbeveiliging 5.4 Compliance in netwerkbeveiliging Module 6: Identiteits- en toegangsbeheer (IAM) met AI 6.1 Basisprincipes van IAM 6.2 AI bij identiteitsverificatie 6.3 Toegangscontrole en AI 6.4 Bedreigingen voor IAM- en AI-oplossingen Module 7: Beveiligingsbeoordeling en incidentrespons met AI 7.1 Technieken voor beveiligingstesten 7.2 AI bij beveiligingstesten 7.3 Continue monitoring en AI 7.4 Planning van incidentrespons 7.5 Beheer van cyberbeveiligingsincidenten 7.6 Juridische en regelgevende overwegingen Module 8: Beveiligingsactiviteiten met AI 8.1 Security Operations Center (SOC) 8.2 Gegevensclassificatie en -bescherming 8.3 Naleving van privacywetgeving 8.4 Disaster Recovery en AI 8.5 AI in beveiligingscoördinatie Module 9: Beveiliging en audit van softwareontwikkeling met AI 9.1 Veilige softwareontwikkelingscyclus (SDLC) 9.2 AI bij het testen van applicatiebeveiliging 9.3 AI in veilige DevOps 9.4 Bedreigingsmodellering en AI 9.5 Interne en externe audits 9.6 Continue monitoring Module 10: Toekomstige trends in AI en naleving van cyberbeveiliging 10.1 Opkomende AI-technologieën 10.2 AI in cyberdreigingsinformatie 10.3 Kwantumcomputers en AI 10.4 Ethische overwegingen en AI-governance 10.5 Praktische toepassingen Optionele module: AI-agenten voor naleving van beveiligingsvoorschriften 1. Wat zijn AI-agenten 2. Belangrijkste mogelijkheden van AI-agenten bij cyberbeveiligingscompliance 3. Toepassingen en trends voor AI-agenten bij naleving van beveiligingsvoorschriften 4. Hoe werkt een AI-agent 5. Kernkenmerken van AI-agenten 6. Soorten AI-agenten Tools die u gaat verkennen Secureframe LeewayHertz Securiti Scytale Inclusief online examen onder toezicht, met één gratis herkansing. Examenopzet: 50 vragen, 70% vereist om te slagen, 90 minuten, online examen onder toezicht Toegang tot alle materialen en examens wordt gedurende 365 dagen na levering verleend. Cursus onder begeleiding van een docent OF cursus in eigen tempo + officieel examen + digitale badge
€530
E-Learning
max 999
5 dagen

AI+ Telecommunications Practitioner™

Formerly known as AI+ Telecommunications™<br> <br>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.  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
€3.930
Klassikaal
max 12
5 dagen

AI+ Telecommunications Practitioner™ eLearning

Formerly known as AI+ Telecommunications™<br> <br>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.  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
€530
E-Learning
max 999
5 dagen

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 Course Overview Course IntroductionPreview Module 1: Foundations of Artificial Intelligence 1.1 Introduction to AI Preview 1.2 Types of Artificial Intelligence Preview 1.3 Branches of Artificial Intelligence 1.4 Applications and Business Use Cases Module 2: Mathematical Concepts for AI 2.1 Linear Algebra Preview 2.2 Calculus Preview 2.3 Probability and Statistics Preview 2.4 Discrete Mathematics Module 3: Python for Developer 3.1 Python Fundamentals Preview 3.2 Python Libraries Module 4: Mastering Machine Learning 4.1 Introduction to Machine Learning 4.2 Supervised Machine Learning Algorithms 4.3 Unsupervised Machine Learning Algorithms 4.4 Model Evaluation and Selection Module 5: Deep Learning 5.1 Neural Networks 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models Module 6: Computer Vision 6.1 Image Processing Basics 6.2 Object Detection 6.3 Image Segmentation 6.4 Generative Adversarial Networks (GANs) Module 7: Natural Language Processing 7.1 Text Preprocessing and Representation 7.2 Text Classification 7.3 Named Entity Recognition (NER) 7.4 Question Answering (QA) Module 8: Reinforcement Learning 8.1 Introduction to Reinforcement Learning 8.2 Q-Learning and Deep Q-Networks (DQNs) 8.3 Policy Gradient Methods Module 9: Cloud Computing in AI Development 9.1 Cloud Computing for AI 9.2 Cloud-Based Machine Learning Services Module 10: Large Language Models 10.1 Understanding LLMs 10.2 Text Generation and Translation 10.3 Question Answering and Knowledge Extraction Module 11: Cutting-Edge AI Research 11.1 Neuro-Symbolic AI 11.2 Explainable AI (XAI) 11.3 Federated Learning 11.4 Meta-Learning and Few-Shot Learning Module 12: AI Communication and Documentation 12.1 Communicating AI Projects 12.2 Documenting AI Systems 12.3 Ethical Considerations Optional Module: AI Agents for Developers 1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents 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
€3.930
Klassikaal
max 12
5 dagen

AI+ Developer™ eLearning

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 Course Overview Course IntroductionPreview Module 1: Foundations of Artificial Intelligence 1.1 Introduction to AI Preview 1.2 Types of Artificial Intelligence Preview 1.3 Branches of Artificial Intelligence 1.4 Applications and Business Use Cases Module 2: Mathematical Concepts for AI 2.1 Linear Algebra Preview 2.2 Calculus Preview 2.3 Probability and Statistics Preview 2.4 Discrete Mathematics Module 3: Python for Developer 3.1 Python Fundamentals Preview 3.2 Python Libraries Module 4: Mastering Machine Learning 4.1 Introduction to Machine Learning 4.2 Supervised Machine Learning Algorithms 4.3 Unsupervised Machine Learning Algorithms 4.4 Model Evaluation and Selection Module 5: Deep Learning 5.1 Neural Networks 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models Module 6: Computer Vision 6.1 Image Processing Basics 6.2 Object Detection 6.3 Image Segmentation 6.4 Generative Adversarial Networks (GANs) Module 7: Natural Language Processing 7.1 Text Preprocessing and Representation 7.2 Text Classification 7.3 Named Entity Recognition (NER) 7.4 Question Answering (QA) Module 8: Reinforcement Learning 8.1 Introduction to Reinforcement Learning 8.2 Q-Learning and Deep Q-Networks (DQNs) 8.3 Policy Gradient Methods Module 9: Cloud Computing in AI Development 9.1 Cloud Computing for AI 9.2 Cloud-Based Machine Learning Services Module 10: Large Language Models 10.1 Understanding LLMs 10.2 Text Generation and Translation 10.3 Question Answering and Knowledge Extraction Module 11: Cutting-Edge AI Research 11.1 Neuro-Symbolic AI 11.2 Explainable AI (XAI) 11.3 Federated Learning 11.4 Meta-Learning and Few-Shot Learning Module 12: AI Communication and Documentation 12.1 Communicating AI Projects 12.2 Documenting AI Systems 12.3 Ethical Considerations Optional Module: AI Agents for Developers 1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents 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
€530
E-Learning
max 999
5 dagen

AI+ Architect Practitioner™

Formerly known as AI+ Architect™<br><br>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 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
€3.930
Klassikaal
max 12
5 dagen

AI+ Architect Practitioner™ eLearning

Formerly known as AI+ Architect™<br><br>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 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
€530
E-Learning
max 999
5 dagen

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 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
€3.930
Klassikaal
max 12
5 dagen

AI+ Engineer™ eLearning

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 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
€530
E-Learning
max 999
5 dagen