AI Techniques for Networking

1h 14mIntermediate2024-06-14

Authors

Ryan Hu

Professor at the Seneca College of Applied Arts and Technology

Course details

Join instructor Ryan Hu in this beginner-friendly course to gather comprehensive insights into AI and machine learning-driven techniques for networking. Ryan explores cutting-edge technologies that seamlessly integrate AI principles into traditional networking frameworks, before diving into more specific topics such as root cause analysis, predictive analytics, and dynamic resource allocation tailored for optimizing network management and performance.

Uncover the potential of using AI-driven solutions to predict and prevent threats and enhance overall system efficiency. With typical and real-world examples, you’ll also get an opportunity to gain practical experience with applying AI techniques to solve everyday networking challenges. Whether you’re a networking professional seeking to stay ahead in the digital age or an AI enthusiast eager to upskill for the future, by the end of this course, you’ll be equipped with the know-how and expertise to navigate the future of intelligent, efficient networking.

Learning objectives
Master AI and machine learning technologies and their practical applications for networking.
Streamline network management and operations with AI.
Utilize AI for network optimization to enhance network performance.
Apply AI to improve network security and resilience.
Explore the challenges and future trends of AI for networking.

Skills covered

Artificial Intelligence FoundationsPersonaNetwork AdministrationArtificial Intelligence (AI)Network and System Administration

Concepts

0. Introduction

01 - Revolutionize networking with AI and machine learning
02 - What you should know

1. Introduction to AI in Networking

03 - AI applications in networking
04 - AI technologies for networking

2. AI-Driven Network Management

05 - Root cause analysis
06 - Predictive analytics for network performance
07 - Intelligent network monitoring and operations

3. AI-Driven Network Optimization

08 - Adaptive routing
09 - Dynamic resource allocation
10 - Load balancing and traffic engineering

4. AI-Driven Network Security and Resilience

11 - Threat detection and prevention
12 - Network resilience assurance

5. Challenges and Future Trends

13 - Current challenges in AI for networking
14 - AI for 6G and beyond

Conclusion

15 - Next steps