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Overview Prerequisites Skills You’ll Gain Self Study Materials Included Tools What You’ll Learn Course Modules FAQs

What’s Included?

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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.

Skills You’ll Gain

  • AI & Machine Learning
  • Telecommunications Technologies (5G, IoT, SDN)
  • Network Security Tools (IDS, Intrusion Detection)
  • Predictive Analytics
  • Big Data Technologies

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You’ll Master

TensorFlow

TensorFlow

Keras

Keras

Matplotlib

Matplotlib

What You’ll Learn

AI and Telecommunications Integration

Learn how AI integrates with telecom technologies to optimize network performance and improve customer experience.

Python for Telecom Applications

Master Python for network optimization, predictive maintenance, and telecom data analysis.

Data Analysis and Network Optimization

Understand how to process telecom data and apply AI for enhanced network reliability and resource management.

AI-Driven Network Management

Apply AI techniques for intelligent traffic management, resource allocation, and real-time network monitoring

Course Modules

Module 1: Introduction to AI in Telecommunications
  1. 1.1 AI Fundamentals in Telecommunications
  2. 1.2 AI Technologies for Telecom
  3. 1.3 Emerging Trends in AI for Telecommunications
  4. 1.4 Case Study
  5. 1.5 Hands-on
Module 2: Data Engineering for Telecom AI
  1. 2.1 Foundation of Telecom Data Engineering
  2. 2.2 Designing and Managing the Telecom Data Pipeline
  3. 2.3 Data Engineering tools and Technology
  4. 2.4 Case Study: SK Telecom’s Big Data Analytics with Metatron Discovery
  5. 2.5  Hands on Exercise
Module 3: AI for 5G Networks
  1. 3.1 Introduction to 5G
  2. 3.2 AI Applications in 5G
  3. 3.3 Enhancing Network Management with AI
  4. 3.4 Case Study
  5. 3.5 Hands-on
Module 4: AI in Network Optimization
  1. 4.1 Predictive Network Management
  2. 4.2 Performance Enhancement Techniques
  3. 4.3 Traffic Management Strategies
  4. 4.4 Case Study
  5. 4.5 Hands-on
Module 5: AI in Network Security
  1. 5.1 Security Threats in Telecom
  2. 5.2 AI Security Solutions
  3. 5.3 Advanced Security Frameworks
  4. 5.4 Case Study
  5. 5.5 Hands-on
Module 6: Enhancing Customer Experience with AI
  1. 6.1 Personalized Customer Service
  2. 6.2 Service Quality Improvement
  3. 6.3 Enhancing Customer Engagement
  4. 6.4 Case Study
  5. 6.5 Hands-on
Module 7: IoT Integration with Telecommunications
  1. 7.1 IoT Fundamentals
  2. 7.2 Managing IoT Security Challenges
  3. 7.3 Enhancing Operational Efficiency with IoT
  4. 7.4 Case Study
  5. 7.5 Hands-on
Module 8: AI-Integrated Network Operations Centers (NOC)
  1. 8.1 Transitioning to AI-driven NOCs
  2. 8.2 Automating escalations and root cause analyses
  3. 8.3 Closed-loop automation with AI and SDN integration
  4. 8.4 Designing AI-ready network architectures
  5. 8.5 Change management strategies for AI rollouts in operations
  6. 8.6 Case Study: Implementation of AI assistants in NOCs
Module 9: Ethical Considerations in Artificial Intelligence
  1. 9.1 Ethical Implications of Using Artificial Intelligence
  2. 9.2 Responsible Deployment Practices
  3. 9.3 Emerging Trends and Challenges
  4. 9.4 Case Study
  5. 9.5 Hands-on
Module 10: Capstone Project

Frequently Asked Questions

Yes, you’ll gain practical, hands-on experience to immediately apply AI and BI skills in real-world business scenarios through case studies and a capstone project.

This course combines AI with telecommunications, teaching Python, machine learning, and network management, focusing on 5G, IoT, and predictive maintenance.

You’ll work on projects like network optimization, predictive maintenance, QoS, and resource management, culminating in a real telecom capstone.

You’ll work on projects like network optimization, predictive maintenance, QoS, and resource management, culminating in a real telecom capstone.

You’ll gain expertise in AI and telecom technologies, with hands-on experience and a capstone preparing you for roles in telecom networks and 5G deployment.