AI for Network Engineers

Focused View

Riad Rahmo

1:54:07

7 View

1. Introduction

1. Course overview.mp4

07:55

2. Artificial intelligence and networking.mp4

06:34

2. Reinforcement learning

1.1 video-3 q-learning.pptx

1. Q-learning.mp4

26:48

2.1 video-4 mapping-the-maze-example-to-a-cisco-network.pptx

2. Mapping the maze example to a Cisco network.mp4

11:37

3.1 video-5 applications-of-reinforcement-learning-in-networking.pptx

3. Applications of reinforcement learning in networking.mp4

12:00

3. Developing a networking reinforcement learning app

1.1 reinforcement-learning-app.zip

1.2 training-script.zip

1.3 video-6 project-overview.pptx

1. Project overview.mp4

03:59

2.1 cisco-r1.txt

2.2 cisco-r2.txt

2.3 create a loopback interface in windows.docx

2.4 IMPORTANT NOTE.docx

2.5 video-7 gns3-lab-environment-overview.pptx

2. GNS3 lab environment.mp4

07:50

3.1 reinforcement-learning-app.zip

3.2 video-8 the-applications-code.pptx

3. The applications code.mp4

17:27

4.1 reinforcement-learning-app.zip

4. Running the application.mp4

09:25

5.1 training-script.zip

5.2 video-10 the-training-script.pptx

5. The training script.mp4

08:40

4. Next steps

1. Next steps.mp4

01:52

Description

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

What You'll Learn?

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

Who is this for?

Networking professionals

Cybersecurity professionals

Systems engineers/admins

IT professionals in general

What You Need to Know?

Beginner level at any programming language

More details

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.
Prerequisites:
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

User Reviews

Rating

average 0

Total votes0

Focused display

Senior network engineer/architect with a successful and demonstrated history of working in the computer networks industry.Skilled at designing, managing, and troubleshooting complex networks of various types, such as: campus, datacenter, cloud, and enterprise networks in general.Strong overall information technology knowledge and experience, for example: programming, automation, systems administration, cybersecurity, virtualization technologies, wireless communications, and more.Academic credentials include a bachelor's degree in electrical engineering, and multiple industry certifications, such as Cisco and Amazon certifications.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.