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AI+ Quality Assurance™ Self-Paced Online Course
USD$700.00 (GST excl.)
Master AI-Driven Quality Assurance: Elevate Your Testing Efficiency, Accuracy, and Scalability
- AI Testing Mastery: Gain hands-on experience with AI-powered testing tools and techniques
- Intelligent Automation Edge: Streamline defect detection and performance testing using intelligent automation
- QA Career Fast-Track: Accelerate your QA career with our comprehensive, industry-aligned exam bundle
Overview
- AI Testing Mastery: Gain hands-on experience with AI-powered testing tools and techniques
- Intelligent Automation Edge: Streamline defect detection and performance testing using intelligent automation
- QA Career Fast-Track: Accelerate your QA career with our comprehensive, industry-aligned exam bundle
Who should enroll
- QA Professionals: Looking to enhance their testing strategies with AI-driven tools and techniques.
- Software Testers: Eager to improve defect detection and automate their testing processes.
- Developers: Interested in integrating AI into the software development lifecycle for better testing efficiency.
- Data Scientists: Wanting to apply AI and machine learning principles to software quality assurance.
- Tech Managers: Seeking to stay ahead of industry trends and lead teams in AI-enhanced QA practices.
Why this certification matters
- Unlock Advanced QA Skills with AI: Integrate AI and machine learning into testing to automate tasks, predict defects, and optimize performance.
- Enhance Testing Efficiency and Accuracy: Use AI tools to speed up defect detection, improve software quality, and reduce manual errors.
- Stay Ahead in a Competitive Market: Equip yourself with in-demand AI skills to meet industry standards and stand out in software testing.
- Future-Proof Your Career: Master AI technologies like NLP and defect prediction, positioning yourself for future growth in QA.
- Real-World Application and Hands-On Experience: Gain practical experience in AI techniques, preparing you to tackle complex QA challenges and improve software quality.
Learning Modules
10 modules
01Module 1: Introduction to Quality Assurance (QA) and AI
1.1 Overview of QA
1.2 Introduction to AI in QA
1.3 QA Metrics and KPIs
1.4 Use of Data in QA
1.2 Introduction to AI in QA
1.3 QA Metrics and KPIs
1.4 Use of Data in QA
02Module 2: Fundamentals of AI, ML, and Deep Learning
2.1 AI Fundamentals
2.2 Machine Learning Basics
2.3 Deep Learning Overview
2.4 Introduction to Large Language Models (LLMs)
2.2 Machine Learning Basics
2.3 Deep Learning Overview
2.4 Introduction to Large Language Models (LLMs)
03Module 3: Test Automation with AI
3.1 Test Automation Basics
3.2 AI-Driven Test Case Generation
3.3 Tools for AI Test Automation
3.4 Integration into CI/CD Pipelines
3.2 AI-Driven Test Case Generation
3.3 Tools for AI Test Automation
3.4 Integration into CI/CD Pipelines
04Module 4: AI for Defect Prediction and Prevention
4.1 Defect Prediction Techniques
4.2 Preventive QA Practices
4.3 AI for Risk-Based Testing
4.4 Case Study: Defect Reduction with AI
4.2 Preventive QA Practices
4.3 AI for Risk-Based Testing
4.4 Case Study: Defect Reduction with AI
05Module 5: NLP for QA
5.1 Basics of NLP
5.2 NLP in QA
5.3 LLMs for QA
5.4 Case Study: Using NLP for Bug Triaging
5.2 NLP in QA
5.3 LLMs for QA
5.4 Case Study: Using NLP for Bug Triaging
06Module 6: AI for Performance Testing
6.1 Performance Testing Basics
6.2 AI in Performance Testing
6.3 Visualization of Performance Metrics
6.4 Case Study: AI in Performance Testing of a Cloud App
6.2 AI in Performance Testing
6.3 Visualization of Performance Metrics
6.4 Case Study: AI in Performance Testing of a Cloud App
07Module 7: AI in Exploratory and Security Testing
7.1 Exploratory Testing with AI
7.2 AI in Security Testing
7.3 Case Study: Enhancing Security Testing with AI
7.2 AI in Security Testing
7.3 Case Study: Enhancing Security Testing with AI
08Module 8: Continuous Testing with AI
8.1 Continuous Testing Overview
8.2 AI for Regression Testing
8.3 Use-Case: Risk-Based Continuous Testing
8.2 AI for Regression Testing
8.3 Use-Case: Risk-Based Continuous Testing
09Module 9: Advanced QA Techniques with AI
9.1 AI for Predictive Analytics in QA
9.2 AI for Edge Cases
9.3 Future Trends in AI + QA
9.2 AI for Edge Cases
9.3 Future Trends in AI + QA
10Module 10: Capstone Project
Tools Covered
TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMaker
| Format | Online, self-paced |
|---|---|
| Duration | Instructor-Led: 5 days (live or virtual) |
| Exam | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Included | Self-paced course + Official exam + Digital badge |
| Prerequisites | Programming Skills, Basics of QA, Foundational knowledge of machine learning concepts |
| Delivery | Projects & case studies |
| Outcome | Industry-recognized credential + hands-on experience |
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