AI+ Telecommunications™
AI in Telecommunications: Redefining the Future of Seamless Connectivity
This course provides foundational insights into how AI technologies enhance telecom networks, covering applications such as predictive maintenance, network optimization, and customer service automation. It explores advanced AI applications in 5G deployment, anomaly detection, and real-time resource management to improve network performance. Learners will gain specialized expertise in AI-driven solutions for cybersecurity, fraud detection, and efficient IoT integration, ensuring network reliability. The course culminates in a capstone project where participants develop AI-driven solutions to address real-world telecom challenges, including network optimization and intelligent service delivery.
Enrollment Fee:
MUR 15,145
What You'll Learn
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
Prerequisites
- Telecommunications Knowledge: Basic understanding of telecommunications concepts, including networks, 5G, and IoT.
- Programming Skills: Familiarity with programming, preferably in Python.
- Data Analysis: Basic knowledge of data analysis techniques is beneficial.
- AI Familiarity: Prior experience with AI is helpful but not required for enrollment in this course.
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 15,145