AI+ Telecommunications™

Course code: AT2501

Price of the certification exam is included in the price of the course.

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.

Akční cena

350 EUR

424 EUR including VAT

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Course dates
Course description
Target group
Course structure
Prerequisites
Certification

Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 350 EUR Akční cena

	Starting date	Type	Course duration	Language		Price without VAT
	Upon request	Self-paced	40 hours	en	A	350 EUR	Register
G	Guaranteed course
A	Akční cena

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Course description

AI-Powered Telecom Innovation

Learn to integrate AI technologies to enhance telecom services, from 5G to cybersecurity.

Practical, Hands-On Learning

Engage in real-world projects, simulating AI applications across various telecom domains.

Cutting-Edge Industry Relevance

Stay ahead of emerging trends like AI-driven network optimization and IoT integration.

Comprehensive Skill Development

Gain expertise in data engineering, AI algorithms, and predictive maintenance for telecom infrastructure.

Ethical & Strategic Insights

Explore ethical considerations in AI deployment and its impact on the telecom industry.

TensorFlow
Keras
Matplotlib

Target group

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.

Course structure

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

Prerequisites

Basic understanding of telecommunications concepts and technologies, familiarity with programming, preferably Python, basic knowledge of data analysis techniques, prior experience with AI.