40 Hours Live and Intensive Training

Module -1

Introduction to AI/ML and Its Relevance to Networking

Overview of AI and ML

• Definition and key concepts
• History and evolution of AI and ML
• Differences between AI, ML, and Deep Learning

AI and ML in the Networking Domain

• Importance of AI and ML in modern networks
• Use cases: Traffic prediction, anomaly detection, network optimization

Basic Concepts of Networking

• Review of essential networking concepts relevant to AI/ML
• The relationship between network data and machine learning

Module -2

Python for AI/ML and Networking

Introduction to Python

• Overview of Python and its use in AI/ML
• Data types, variables, and operators
• Control structures (loops, conditions, functions)
• Working with files and handling exceptions
• Python libraries for data science: NumPy, Pandas, Matplotlib
• Python libraries for network automation: Netmiko, Ncclient, Requests

Working with Network Data 

• Parsing and analyzing network logs
• Basic data manipulation techniques with Pandas

Module -3

Data Handling and Processing

Understanding Network Data Formats

• JSON, XML, and YAML in network automation
• Syslogs, SNMP data, and NetFlow records

Working with Data in Python

• Using Pandas for data manipulation and cleaning
• Parsing JSON and XML configurations
• Storing network data in CSV and databases

Data Visualization for Network Insights

• Using Matplotlib and Seaborn to plot network performance data
• Creating dashboards to monitor traffic trends and latency patterns

Module -4

Fundamentals of Machine Learning

Understanding Machine Learning

• Supervised vs. Unsupervised learning
• Key algorithms: Linear regression, decision trees, k-means clustering

Feature Engineering for Network Data 

• Extracting meaningful features from network logs
• Preprocessing data: Normalization, handling missing values

Module 5

Introduction to Predictive AI for Networking

What is Predictive AI?

• Predictive analytics and machine learning
• How predictive AI differs from traditional machine learning

Network Data for Predictive AI

• Collecting and processing network data
• Feature selection and engineering

Predictive AI Use Cases in Networking

• Traffic prediction
• Network failure forecasting
• Load balancing optimization

Module – 6

Fundamentals of Language Models and AI

Introduction to Language Models (LMs):

• What are language models (LMs), and how do they work?
• The architecture behind LMs: Large Language Models (LLMs), n-grams, RNNs, and Transformers
• The importance of large-scale datasets for training LMs

Creating Language Models:

• Overview of the process to train language models
• Fine-tuning LMs for specific use cases

Applications of Language Models in Networking:

• Automated log analysis, chatbots for network support
• AI-driven configuration writing using natural language input.

Module 7

Generative AI and its Application in Network Engineering

Introduction to Generative AI

• What is Generative AI?
• Overview of Generative Adversarial Networks (GANs) and Transformer models
• Generative AI’s role in simulating network environments

Generative AI for Network Configuration and Scripting

• Automated generation of network scripts
• AI-driven configuration templates for complex networks