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AI+ Network™ Spanish
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Introduction
Course Introduction
Module 1: Networking Foundations
1.1 Understanding Network Types (LAN, WAN, MAN, PAN)
1.2 Networking Topologies and Architectures
1.3 Network Components and Devices
1.4 OSI and TCP/IP Models
1.5 Common Protocols (HTTP, HTTPS, FTP, SMTP, DNS)
1.6 Wireless Networking Standards (Wi-Fi, Bluetooth)
1.7 Structured Cabling Systems
1.8 IP Addressing & Network Segmentation
1.9 Basic Security Concepts and Terminologies
1.10 Firewalls and Network Security Appliances
1.11 Security Policies and Best Practices
Module 2: Advanced Networking Technologies
2.1 Virtual LANs (VLANs) and Virtual Networks (VNETs)
2.2 Software-Defined Networking (SDN)
2.3 Network Functions Virtualization (NFV)
2.4 5G Networking and Beyond
2.5 Internet of Things (IoT) Networking: A Comprehensive Overview
2.6 Edge Computing and Fog Networking
2.7 Dynamic Routing Protocols: OSPF, BGP, and EIGRP
2.8 Layer 3 Switching and Multicast Routing
2.9 Advanced Traffic Engineering and QoS
2.10 Storage Area Networks (SANs) and Network Attached Storage (NAS)
2.11 Data Centre Networking and Design
2.12 Cloud Computing : A Detailed Overview
2.13 Virtualization: A Deatailed Overview
Module 3: AI in Networking
3.1 Artificial Intelligence (AI),Machine Learning (ML) and Deep Learning (DL)
3.2 Types of Artificial Intelligence
3.3 Advantages and disadvantages of AI
3.4 Machine Learning Techniques - Supervised Learning
3.5 Machine Learning Techniques - Unsupervised Learning
3.6 Machine Learning Techniques - Semi-Supervised Learning and Reinforcement Learning
3.7 AI Frameworks
3.8 AI Frameworks- TenserFlow
3.9 AI Frameworks- PyTorch
3.10 Network Performance Optimization with AI
3.11 Traffic Analysis and Load Balancing using AI
3.12 AI Techniques in Traffic Analysis
3.13 AI Techniques in Load balancing
3.14 AI-Powered Network Diagnostics and Troubleshooting
3.15 Key AI Algorithms and Techniques for Network Troubleshooting
3.16 AI for Network Security and Threat Detection
3.17 Machine Learning for Intrusion Detection
3.18 Anomaly Detection and Threat Intelligence
3.19 Tools for Threat Intelligence
3.20 AI in Security Information and Event Management (SIEM)
3.21 AI-Powered Network Automation: Benefits and Impact
3.22 Predictive Network Maintenance and Monitoring
3.23 AI-Powered Network Configuration and Management
Module 4: Network Automation and Orchestration
4.1 Fundamentals of Network Automation
4.2 Automation Tools and Technologies- Ansible
4.3 Automation Tools and Technologies- Puppet
4.4 Automation Tools and Technologies- Chef
4.5 Scripting and Automation with Python
4.6 Common Libraries of Python for Network Automation
4.7 Best Practices for Network Automation with Python
4.8 Introduction to Network APIs and Protocols (REST, NETCONF)
4.9 Intent-Based Networking and Automation
4.10 AI in Network Configuration Management
4.11 How AI is implemented in NCM?
4.12 Orchestration Platforms and Tools-Kubernetes
4.13 Orchestration Platforms and Tools-OpenStack
4.14 Policy-Based Network Access Control (PNAC)
4.15 AI-Driven Dynamic Policy Enforcement: Advantages and Applications
4.16 Role-Based Access Control
4.17 Zero Trust Architecture
4.18 Real-World Network Automation Scenarios
4.19 Industry Use Cases and Best Practices
4.20 Future Trends in Network Automation
Module 5: AI-Enhanced Network Security
5.1 AI Techniques for Malware Detection
5.2 What is the Importance of AI in Malware Detection?
5.3 Features Used in Malware Detection via Pattern Recognition
5.4 Behavioral Analysis and User Profiling
5.5 How Behavioral Analysis Leverages AI to Enhance Security and Detect Threats
5.6 Enhancing Security Measures with AI
5.7 AI in Incident Response and Threat Hunting
5.8 Use Cases of AI-Powered Incident Response
5.9 Threat Hunting
5.10 Designing Secure Networks with AI
5.11 Key Components of AI-Driven Secure Network Design
5.12 Zero Trust and AI-Based Access Control
5.13 Integrating AI with Zero Trust Principles for Enhanced Access Control
5.14 AI in Network Segmentation and Microsegmentation
5.15 AI Techniques in Network Segmentation and Microsegmentation
5.16 Benefits of AI-Driven Network Segmentation
5.17 AI in Security Information and Event Management (SIEM)
5.18 How to implement AI in SIEM?
5.19 Real-World Applications and Use Cases of AI in SIEM
5.20 Predictive Analytics for Cyber Threat Intelligence
5.21 Predictive Analytics in Cyber Threat Intelligence: Common Techniques
5.22 Applications of Predictive Analytics in Cyber Threat Intelligence
5.23 AI-Driven Security Operations Centers (SOC)
5.24 How AI Works in an AI-Driven SOC
5.25 Ethical Data Privacy Practices
5.26 Balancing Security with Privacy: Ethical Strategies
5.27 Legal and Regulatory Considerations in Data Privacy
5.28 AI Bias and Fairness in Security Applications
5.29 Overcoming Algorithmic Bias for Equitable Security
5.30 Compliance & Security Standards
Module 6: Practical Labs and Hands-On Projects
6.1 Introduction
Module 7: Emerging Trends and Future Directions
7.1 AI and 5G: The Next Generation of Networking
7.2 Role of AI in 5G Networks
7.3 How to implement AI in 5G networks?
7.4 AI-Quantum Integration: Revolutionizing Networking Together
7.5 Use cases of AI driven Quantum Computing
7.6 AI for Smart Cities and IoT Solutions
7.7 What Is IoT?
7.8 Real-world Applications of IoT in Smart Cities
7.9 Managing IoT Networks with AI
7.10 AI-Driven IoT Network Management Use Cases
7.11 Blockchain for Network Security and Management
7.12 Advantages of AI driven Blockchain for Network Security and Management
7.13 Certifications and Career Paths in AI Networking
Course Summary
Course Summary
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AI+ Network™ Spanish
