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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.
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™
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 Practitioner™
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 Practitioner™ 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™
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™
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 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
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 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
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
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
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
1.1 Artificial Intelligence Refresher
1.2 Quantum Computing Refresher
Module 2: Quantum Computing Gates, Circuits, and Algorithms
2.1 Quantum Gates and their Representation
2.2 Multi Qubit Systems and Multi Qubit Gates
Module 3: Quantum Algorithms for AI
3.1 Core Quantum Algorithms
3.2 QFT and Variational Quantum Algorithms
Module 4: Quantum Machine Learning
4.1 Algorithms for Regression and Classification
4.2 Algorithms for Dimensionality and Clustering
Module 5: Quantum Deep Learning
5.1 Algorithms for Neural Networks – Part I
5.2 Algorithms for Neural Networks – Part II
Module 6: Ethical Considerations
6.1 Ethics for Artificial Intelligence
6.2 Ethics for Quantum Computing
Module 7: Trends and Outlook
7.1 Current Trends and Tools
7.2 Future Outlook and Investment
Module 8: Use Cases & Case Studies
8.1 Quantum Use Cases
8.2 QML Case Studies
Module 9: Workshop
9.1 Project – I: QSVM for Iris Dataset
9.2 Project – II: VQC/QNN on Iris Dataset
9.3 Bonus: IBM Quantum Computers
Optional Module: AI Agents for Quantum
1. What Are AI Agents
2. Key Capabilities of AI Agents in Quantum Computing
3. Applications and Trends for AI Agents in Quantum Computing
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents
Tools you will explore
IBM Qiskit
D-Wave Leap
Google TensorFlow Quantum (TFQ)
Amazon Braket
Online proctored exam included, with one free retake.
Exam format:
50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.930
Klassikaal
max 12
5 dagen
AI+ Quantum Practitioner™ eLearning
Formerly known as AI+ Quantum™
Harness Quantum Power with AI
AI + Quantum Integration: Explore Quantum Gates, Circuits, and AI applications
Advanced Learnings: Includes Quantum Deep Learning and transformative AI methodologies
Industry-Oriented: Real-world case studies and trend analysis
Ethical Focus: Learn implications of quantum AI responsibly and efficiently
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
1.1 Artificial Intelligence Refresher
1.2 Quantum Computing Refresher
Module 2: Quantum Computing Gates, Circuits, and Algorithms
2.1 Quantum Gates and their Representation
2.2 Multi Qubit Systems and Multi Qubit Gates
Module 3: Quantum Algorithms for AI
3.1 Core Quantum Algorithms
3.2 QFT and Variational Quantum Algorithms
Module 4: Quantum Machine Learning
4.1 Algorithms for Regression and Classification
4.2 Algorithms for Dimensionality and Clustering
Module 5: Quantum Deep Learning
5.1 Algorithms for Neural Networks – Part I
5.2 Algorithms for Neural Networks – Part II
Module 6: Ethical Considerations
6.1 Ethics for Artificial Intelligence
6.2 Ethics for Quantum Computing
Module 7: Trends and Outlook
7.1 Current Trends and Tools
7.2 Future Outlook and Investment
Module 8: Use Cases & Case Studies
8.1 Quantum Use Cases
8.2 QML Case Studies
Module 9: Workshop
9.1 Project – I: QSVM for Iris Dataset
9.2 Project – II: VQC/QNN on Iris Dataset
9.3 Bonus: IBM Quantum Computers
Optional Module: AI Agents for Quantum
1. What Are AI Agents
2. Key Capabilities of AI Agents in Quantum Computing
3. Applications and Trends for AI Agents in Quantum Computing
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents
Tools you will explore
IBM Qiskit
D-Wave Leap
Google TensorFlow Quantum (TFQ)
Amazon Braket
Online proctored exam included, with one free retake.
Exam format:
50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€530
E-Learning
max 999
5 dagen