Pearson – AI for Network Engineer
English | Tutorial | Size: 2.37 GB
AI Fundamentals for building AI-enabled Networks
Focus on practical implementation, bridging the gap between theoretical AI concepts and real-world network engineering applications.
Cross-functional expertise, delivered by instructor with dual backgrounds in both network engineering and AI/ML implementation.
Foundational AI concepts, delivered keeping network engineers in mind and illustrating the challenges that they will be facing when designing AI-enabled networks.
This specialized training course equips network engineers with practical AI skills directly applicable to enterprise networking challenges. Moving beyond theoretical concepts, participants will work with actual network datasets to implement AI-driven solutions for automation, anomaly detection, and predictive maintenance. The course combines fundamental AI/ML concepts with hands-on labs using popular tools such as TensorFlow, PyTorch, and network-specific AI platforms to solve common networking problems including traffic analysis, security threat detection, and intelligent capacity planning.
In this 2-day training program, Day 1 focuses on foundational AI concepts for network engineers, data collections and preparation, understanding basic ML models for network engineers. Day 2 covers advanced topics such as Deep Learning for network analysis, NLP for network operations, and understanding LLMs for advanced and AI integrated automation.
What you’ll learn and how you can apply it
Implement basic machine learning models for network traffic anomaly detection and performance prediction.
Develop automated network configuration systems enhanced with AI-driven decision support.
Apply natural language processing techniques to network logs and alerts for improved troubleshooting.
Integrate AI tools with existing network monitoring platforms to create predictive maintenance workflows.
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