Course Overview
TOPThe AI+ Quality Assurance™ course empower QA professionals with cutting-edge AI skills. It covers AI/ML fundamentals, test automation, defect prediction, NLP applications, and AI-driven security and performance testing. Learners gain hands-on experience with tools like Scikit-learn, Testim.io, and LLMs for test case generation and bug triaging.
Scheduled Classes
TOP| 01/26/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 02/23/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 03/16/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 04/20/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 05/18/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 06/29/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand how AI transforms traditional quality assurance practices
- Learn to apply ML and automation in defect detection and testing
- Explore AI integration frameworks for QA efficiency and accuracy
- Evaluate AI models for validation and continuous QA improvement
- Address ethical and compliance aspects of AI in QA processes
Outline
TOPIntroduction to Quality Assurance (QA) and AI
- Overview of QA
- Introduction to AI in QA
- QA Metrics and KPIs
- Use of Data in QA
Fundamentals of AI, ML, and Deep Learning
- AI Fundamentals
- Machine Learning Basics
- Deep Learning Overview
- Introduction to Large Language Models (LLMs)
Test Automation with AI
- Test Automation Basics
- AI-Driven Test Case Generation
- Tools for AI Test Automation
- Integration into CI/CD Pipelines
AI for Defect Prediction and Prevention
- Defect Prediction Techniques
- Preventive QA Practices
- Test Automation Basics
- Use of AI for Continuous Monitoring
NLP for QA
- Basics of NLP
- NLP in QA
- Large Language Models for QA
- NLP for Bug Resolution and Analysis
AI for Performance Testing
- Performance Testing Basics
- AI in Performance Testing
- Visualization of Performance Metrics
- AI for Predictive Load Balancing
AI in Exploratory and Security Testing
- Exploratory Testing with AI
- AI in Security Testing
- Advanced Techniques in Security Testing
- AI for Threat Analytics
Continuous Testing with AI
- Continuous Testing Overview
- AI for Regression Testing
- Advanced Continuous Testing Techniques
- Use-Case: Risk-Based Continuous Testing
Advanced QA Techniques with AI
- AI for Predictive Analytics in QA
- AI for Edge Cases
- Future Trends in AI with QA
- Integration of Emerging Technologies
Prerequisites
TOPRequired
- Basic knowledge of Python and familiarity with software testing lifecycle and tools
- Basic knowledge of Quality Assurance principles and practices
Recommended
- Foundational knowledge of machine learning concepts is beneficial but not mandatory
Who Should Attend
TOP- Penetration Tester
- Vulnerability Assessment & Penetration Testing Consultant
- Automation Architect
