What’s Included?
High-Quality Video, E-book & Audiobook
Modules Quizzes
AI Mentor
Access for Tablet & Phone
Prerequisites
- Programming Skills: Basic knowledge of Python and familiarity with Software Testing.
- Basics of Quality Assurance (QA): Foundational knowledge of QA principles and practices.
- Basics of AI: A basic understanding of machine learning concepts is beneficial but not mandatory.
Self Study Materials Included
Videos
Engaging visual content to enhance understanding and learning experience.
Podcasts
Insightful audio sessions featuring expert discussions and real-world cases.
Audiobooks
Listen and learn anytime with convenient audio-based knowledge sharing.
E-Books
Comprehensive digital guides offering in-depth knowledge and learning support.
Module Wise Quizzes
Interactive assessments to reinforce learning and test conceptual clarity.
Additional Resources
Supplementary references and list of tools to deepen knowledge and practical application.
Tools You’ll Master
TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMaker
What You’ll Learn
QA Fundamentals
Understand the core principles of Quality Assurance (QA), including testing methodologies, tools, and processes to ensure software quality.
Manual Testing
Master manual testing techniques, including test case creation, test execution, and defect reporting to ensure software functionality meets requirements.
Automation Testing
Learn automation testing using popular tools like Selenium, Appium, and TestNG, and understand how automation enhances testing efficiency and accuracy.
Performance Testing
Gain expertise in performance testing tools like JMeter and LoadRunner, and learn how to evaluate software performance under different conditions.
Course Modules
Module 1: Introduction to Quality Assurance and AI
- 1.1 Introduction to Quality Assurance (QA) and AI
- 1.2 Introduction to AI in QA
- 1.3 QA Metrics and KPIs
- 1.4 Use of Data in QA
Module 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)
Module 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
Module 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
Module 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
Module 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
Module 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
Module 8: Continuous Testing with AI
- 8.1 Continuous Testing Overview
- 8.2 AI for Regression Testing
- 8.3 Use-Case: Risk-Based Continuous Testing
Module 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
Module 10: Capstone Project
Frequently Asked Questions
Yes, the course is suitable for individuals who are new to QA, as it starts with the basics and gradually builds up to more advanced concepts like AI integration into testing.
Yes, the course covers industry-standard AI tools and platforms used for test automation, defect prediction, performance testing, and more, ensuring you stay up to date
Upon completion, you will have a portfolio of hands-on projects, including the capstone project, which showcases your ability to apply AI in QA, making you highly competitive
Yes, the course includes case studies and hands-on activities involving cloud applications, helping you leverage AI for performance and scalability testing
You’ll work on projects that include defect prediction, automation of regression tests, performance testing in cloud environments, and applying AI for security testing