Course curriculum

  • 1

    Important Lab Instructions

    • Important Lab Instructions
  • 2

    Artificial Intelligence

    • Lab 1 - AI-Driven Vulnerability Detection with OpenVAS and Tensorflow

    • Lab 2 - Objectives_Automated CI_CD Pipeline Optimization using Jenkins and MLflow

    • Lab 3 - Monitoring & Alerting with Prometheus, Grafana, and AI

    • Lab 4 - NLP-driven Automated Compliance Reporting with ELK Stack

    • Lab 5 - Risk Assessment with AI and OpenFAIR

    • Lab 6 - Self-Healing Systems with Kubernetes and AI

    • Lab 7 - AI-Enhanced Firewall Rules Optimization with pfSense and Scikit-learn

    • Lab 8 - Automated Data Classification for GDPR with Python and NLP

    • Lab 9 - AI-Driven Intrusion Detection with Snort and PyTorch

    • Lab 10 - Application Security Enhancement using OWASP ZAP and AI
  • 3

    Python Programming for Machine Learning II

    • Lab 1 - Secure Infrastructure as Code (IaC) with Terraform and AWS

    • Lab 2 - Continuous Integration_Continuous Deployment (CI_CD) with Jenkins and GitHub

    • Lab 3 - Implementing Role-Based Access Control (RBAC) with Kubernetes

    • Lab 4 - Security Incident and Event Management (SIEM) with ELK Stack

    • Lab 5 - Integrate Open Policy Agent (OPA) with Jenkins for Compliance Checks

    • Lab 6 - Set Up Docker Environment with Clair for Vulnerability Scanning

    • Lab 7 - Create AWS AMI with Packer and Ansible, and Deploy an EC2 Instance

    • Lab 8 - Setting Up Isolated VPCs in AWS and Implementing Network Policies in Kubernetes with Calico

    • Lab 9 - Deploy Prometheus in Kubernetes and Visualize Metrics in Grafana

    • Lab 10 - Designing a Serverless Function with AWS Lambda and Implementing Security Policies
  • 4

    Advanced Topics in Machine Learning II

    • Lab 1 - Configuring an Open-Source Linux Distribution for Machine Learning

    • Lab 2 - Understanding and Implementing a Basic GAN Using TensorFlow

    • Lab 3 - Containerizing a Trained ML Model and Deploying it on Kubernetes

    • Lab 4 - Installing and Configuring Apache Airflow for ML Workflows

    • Lab 5 - Implementing Zero Trust with SPIFFE_SPIRE for Securing ML Model Deployment

    • Lab 6 - Setting Up a GitLab Runner and CI_CD Pipeline for an ML Project

    • Lab 7 - Installing and Configuring OpenAI Gym, Implementing a Reinforcement Learning Agent, and Training the Agent

    • Lab 8 - Installing Hugging Face Library, Fine-Tuning a Transformer Model, and Deploying It

    • Lab 9 - Setting Up Horovod for Distributed Deep Learning

    • Lab 10 - Implementing and Interpreting a Deep Learning Model for Fairness using SHAP