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AI+ Telecommunications Practitioner™
- 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.
Certificate Code
AT - 2501
Exam Format
AI-Driven Remote Exam Proctoring
Course Overview
Important details and certification information
Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic understanding of telecommunications concepts and technologies, familiarity with programming, preferably Python, basic knowledge of data analysis techniques, prior experience with AI.
50 questions, 70% passing, 90 minutes, online proctored exam
Certification Modules
- 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
- 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
- 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
- 4.1 Predictive Network Management
- 4.2 Performance Enhancement Techniques
- 4.3 Traffic Management Strategies
- 4.4 Case Study
- 4.5 Hands-on
- 5.1 Security Threats in Telecom
- 5.2 AI Security Solutions
- 5.3 Advanced Security Frameworks
- 5.4 Case Study
- 5.5 Hands-on
- 6.1 Personalized Customer Service
- 6.2 Service Quality Improvement
- 6.3 Enhancing Customer Engagement
- 6.4 Case Study
- 6.5 Hands-on
- 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
- 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
- 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