AI for Network Engineers

person icon Riad Rahmo

AI for Network Engineers

AI-Reinforcement learning for creating Python applications to manage networks and systems (Cisco, Juniper, Palo Alto, AWS, etc.).

updated on icon Updated on Nov, 2023

language icon Language - English

person icon Riad Rahmo

architecture icon Artificial Intelligence,Cisco,IT Networking Fundamentals,Python,Cyber Security

Lectures -11

Resources -15

Duration -1.5 hours


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Course Description

In an era where organizations are increasingly integrating AI solutions into their operations, it is essential for networking professionals, regardless of their experience level, to grasp the concepts of reinforcement learning and Q-learning. This comprehensive course is designed to provide engineers with the fundamental knowledge and skills needed to understand, apply, and adapt these cutting-edge technologies to address the evolving challenges in networking.

As AI continues to shape the future of technology, the demand for network engineers who can harness the power of reinforcement learning and Q-learning is on the rise. This course delves into the core principles of these methodologies, offering a deep exploration of how they can be leveraged in the realm of networking, while emphasizing their potential applications in fields such as cybersecurity, systems administration, and more.

This course is suitable for network engineers at all experience levels, from junior professionals looking to expand their skill set to seasoned experts aiming to stay current with the latest industry trends. It is ideal for individuals seeking to harness the potential of reinforcement learning and Q-learning in networking, cybersecurity, systems administration, and related fields.


Basic knowledge of networking concepts is recommended. Familiarity with Python programming is advantageous but not mandatory.

Course Benefits:

Upon completion of this course, participants will be well-equipped to:

  • Understand the fundamentals of reinforcement learning and Q-learning.

  • Apply these AI methodologies to address networking challenges and optimize network operations.

  • Identify opportunities for AI integration in various aspects of networking, including cybersecurity and systems administration.

  • Effectively design, implement, and manage AI-driven networking solutions.

Who this course is for:

  • Networking professionals
  • Cybersecurity professionals
  • Systems engineers/admins
  • IT professionals in general


What will you learn in this course:

  • Reinforcement learning
  • Q-learning
  • Mapping the concepts of Q-learning to networking challenges
  • Training Q-learning applications
  • Building a reinforcement learning application and a training script


What are the prerequisites for this course?

  • Beginner level at any programming language

AI for Network Engineers


Check out the detailed breakdown of what’s inside the course

2 Lectures
  • play icon Course Overview 07:55 07:55
  • play icon Artificial Intelligence and Networking 06:34 06:34
Reinforcement-Learning (RL)
3 Lectures
Developing a Python Reinforcement-Learning application for a Networking Challenge
5 Lectures
1 Lectures

Instructor Details

Riad Rahmo

Riad Rahmo

Experienced senior network engineer/architect with over a decade of proven expertise in diverse networking environments and industries. Backed by a bachelor’s degree in electronics and communications engineering and a track record of success in network design, security, and automation. Holder of multiple esteemed networking certifications, demonstrating a commitment to continuous professional development.

Current position: Senior network architect.

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