TRAINING COURSE
AI in Network Engineering: Current Technologies and Practical Applications
STARTING TWICE A MONTH
DURATION 7 WEEKS
‎‎ ‎7 200 €
This course caters to network engineers, IT professionals, students in computer science, and technology managers. It's designed to integrate AI into network management for optimizing operations, enhancing security, and improving efficiency. The program provides the latest AI tools and insights essential for advancing in network engineering.
AI Integration
Security Enhancement
Efficiency Improvement
Training Course Program
STARTING TWICE A MONTH
Lecture 1:
Introduction to AI and its application in Network Engineering
  • Definition of AI and its role in the modern world
  • Overview of the application of AI
  • Examples of successful use of AI in network engineering
Lecture 2:
Machine Learning in Network Engineering: Optimization and Data Analysis
  • Introduction to ML and its role in network engineering
  • Types of problems solved by ML in network engineering
  • Application of ML techniques to network optimization and data analysis
Lecture 3:
Deep Learning and Neural Networks in Network Engineering
  • Introduction to DL and NNs
  • Application of convolutional NNs to analyze network data
  • Solving classification and prediction problems using recurrent NNs
Lecture 4:
Using AI to automate network management
  • The role and benefits of automating network management using AI
  • Examples of intelligent network management systems
  • Design and implementation of AI-based automated network solutions
Lecture 5:
Applications of robotization and AI in network security
  • The role of robotization and AI in network security
  • Using AI to detect and prevent cyberattacks
  • Automated real-time threat response systems
Lecture 6:
Optimizing Network Infrastructure with AI
  • Using AI to optimize network configurations and resources
  • Automated scaling and load balancing of network resources
  • Energy and resource management in distributed network systems
Lecture 7:
Network Event Prediction and Data Analytics
  • Using data analytics and forecasting to optimize the performance of networked systems
  • Developing network load and performance prediction models
  • Applying ML algorithms to analyze and interpret network data
Lecture 8:
Ethical and Legal Aspects of AI Application in Network Engineering
  • Examination of ethical issues related to the use of AI in network engineering
  • Discussion of the legal aspects governing the application of AI in network engineering
  • Development and implementation of data security and privacy standards in AI-based network systems
Practical Session 1:
Introduction to ML tools and technologies in network engineering
  • Exploring basic libraries and tools for working with ML in Python (e.g. TensorFlow, Scikit-learn)
  • Practical exercises on processing and analyzing network data using ML algorithms
Practical Session 2:
Designing and training NNs to analyze network data
  • Creating NNs using the TensorFlow/Keras library
  • Training NN models on network traffic datasets
  • Evaluate the performance and quality of the developed models
Practical Session 3:
Development of Automated Network Management Systems Based on AI
  • Creating a prototype of an automated network management system using Python and ML libraries
  • Integration of the developed system with the existing network infrastructure
  • Testing and analyzing the results of the automated system
Practical Session 4:
Using Data Analytics to Optimize Network Processes
  • Developing data analytics models for network load forecasting
  • Analyze network data using Python libraries (e.g., Pandas, Matplotlib)
  • Create reports and visualizations of data analysis results
What kind of result will you get
Participants can expect to acquire essential skills in integrating AI into network management, fortifying security measures, and refining operational efficiency. This training enables them to optimize network performance, proactively identify and address security threats, and streamline day-to-day operations with greater effectiveness.
Student feedback on the course
AI integration transformed how I manage networks. Highly recommend for anyone in IT.
Rein Rand
Practical and insightful! Learned AI-driven security measures that immediately improved our network defenses
Eha Laanes
Game-changer! Efficiency soared after implementing AI techniques taught in this course.
Toomas Tamm
Predictive analytics skills gained here saved us time and resources.
Ene Põld
Fantastic program! Now equipped with cutting-edge AI knowledge, I feel confident tackling any network challenge.
Kadri Kuusk
What you will get after the course
  1. Proficiency in integrating AI technologies into network management systems, enabling automation and predictive analytics for enhanced performance.
  2. Understanding of AI-driven tools and techniques for threat detection, enabling proactive security measures and rapid response to cyber threats.
  3. Ability to streamline network operations through AI-driven optimization, reducing downtime and improving resource utilization.
  4. Capability to predict network behavior and anticipate potential issues, allowing for proactive maintenance and smoother operations.
  5. Enhanced qualifications and expertise in AI applications for network engineering, providing a competitive edge and opening up new career opportunities in the rapidly evolving field.
Official school certificate
Recordings of all lectures
10% discount on any of our courses
Price without discount 8 000 €
7 200 €
Course fees
If you register early (over a month prior to the beginning of the course), you will receive a 10% discount on the entire program.
Register for the course and get 10% off!
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"Cosmopolitacademy" LLC
264.110.1320023
TIN: 08256519
Komitas 19/1, Erevan, Armenia