Certified Professional in AI-driven Quality Assurance
-- viewing nowAI-driven Quality Assurance is a rapidly evolving field that requires professionals to stay ahead of the curve. As a Certified Professional in AI-driven Quality Assurance, you will be equipped to design and implement AI-powered testing frameworks that ensure software quality and reliability.
3,535+
Students enrolled
GBP £ 149
GBP £ 215
Save 44% with our special offer
About this course
With a focus on machine learning, natural language processing, and data analytics, this certification will help you develop the skills needed to identify and mitigate defects in AI-driven systems.
Our certification program is designed for quality assurance professionals and software developers looking to upskill and reskill in the AI era.
Join our community of innovators and take the first step towards a career in AI-driven Quality Assurance. Explore our program today and discover a world of possibilities!
100% online
Learn from anywhere
Shareable certificate
Add to your LinkedIn profile
2 months to complete
at 2-3 hours a week
Start anytime
No waiting period
Course details
•
Machine Learning (ML) Fundamentals: This unit covers the basics of ML, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. It's essential for understanding the AI-driven quality assurance framework. •
Artificial Intelligence (AI) Principles: This unit delves into the principles of AI, including intelligent agents, knowledge representation, and reasoning. It provides a solid foundation for understanding the AI-driven quality assurance approach. •
Data Preprocessing and Cleaning: This unit focuses on the importance of data quality in AI-driven quality assurance. It covers data preprocessing techniques, data cleaning, and data transformation to ensure high-quality data for model training. •
Model Evaluation and Validation: This unit emphasizes the importance of model evaluation and validation in AI-driven quality assurance. It covers metrics for model evaluation, cross-validation, and model selection to ensure accurate and reliable results. •
Natural Language Processing (NLP) for Quality Assurance: This unit explores the application of NLP in quality assurance, including text analysis, sentiment analysis, and entity extraction. It's essential for understanding the role of NLP in AI-driven quality assurance. •
Automated Testing and Validation: This unit covers the principles of automated testing and validation in AI-driven quality assurance. It includes techniques for automated testing, test data generation, and test automation frameworks. •
DevOps and Continuous Integration/Continuous Deployment (CI/CD): This unit focuses on the integration of AI-driven quality assurance with DevOps practices. It covers CI/CD pipelines, continuous monitoring, and continuous improvement. •
AI-Driven Quality Metrics and KPIs: This unit explores the development of AI-driven quality metrics and KPIs, including metrics for model performance, data quality, and process efficiency. •
Human-AI Collaboration and Feedback: This unit emphasizes the importance of human-AI collaboration and feedback in AI-driven quality assurance. It covers techniques for human-AI collaboration, feedback mechanisms, and human-AI team dynamics. •
AI Ethics and Governance: This unit covers the essential aspects of AI ethics and governance, including fairness, transparency, and accountability. It's crucial for ensuring that AI-driven quality assurance practices are aligned with ethical standards and regulatory requirements.
Machine Learning (ML) Fundamentals: This unit covers the basics of ML, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. It's essential for understanding the AI-driven quality assurance framework. •
Artificial Intelligence (AI) Principles: This unit delves into the principles of AI, including intelligent agents, knowledge representation, and reasoning. It provides a solid foundation for understanding the AI-driven quality assurance approach. •
Data Preprocessing and Cleaning: This unit focuses on the importance of data quality in AI-driven quality assurance. It covers data preprocessing techniques, data cleaning, and data transformation to ensure high-quality data for model training. •
Model Evaluation and Validation: This unit emphasizes the importance of model evaluation and validation in AI-driven quality assurance. It covers metrics for model evaluation, cross-validation, and model selection to ensure accurate and reliable results. •
Natural Language Processing (NLP) for Quality Assurance: This unit explores the application of NLP in quality assurance, including text analysis, sentiment analysis, and entity extraction. It's essential for understanding the role of NLP in AI-driven quality assurance. •
Automated Testing and Validation: This unit covers the principles of automated testing and validation in AI-driven quality assurance. It includes techniques for automated testing, test data generation, and test automation frameworks. •
DevOps and Continuous Integration/Continuous Deployment (CI/CD): This unit focuses on the integration of AI-driven quality assurance with DevOps practices. It covers CI/CD pipelines, continuous monitoring, and continuous improvement. •
AI-Driven Quality Metrics and KPIs: This unit explores the development of AI-driven quality metrics and KPIs, including metrics for model performance, data quality, and process efficiency. •
Human-AI Collaboration and Feedback: This unit emphasizes the importance of human-AI collaboration and feedback in AI-driven quality assurance. It covers techniques for human-AI collaboration, feedback mechanisms, and human-AI team dynamics. •
AI Ethics and Governance: This unit covers the essential aspects of AI ethics and governance, including fairness, transparency, and accountability. It's crucial for ensuring that AI-driven quality assurance practices are aligned with ethical standards and regulatory requirements.
Career path
Certified Professional in AI-driven Quality Assurance
Job Market Trends in the UK
| Role | Job Description |
|---|---|
| Ai/ML Engineer | Design and develop artificial intelligence and machine learning models to improve quality assurance processes. Collaborate with cross-functional teams to integrate AI/ML solutions into existing systems. |
| Quality Assurance Engineer | Develop and execute quality assurance plans to ensure software meets industry standards. Conduct testing, analysis, and reporting to identify areas for improvement. |
| Data Scientist | Analyze complex data sets to identify trends and patterns. Develop predictive models to inform quality assurance decisions and drive business growth. |
| Business Analyst | Work with stakeholders to identify business needs and develop solutions to improve quality assurance processes. Analyze data to inform business decisions and drive growth. |
| IT Project Manager | Oversee the planning, execution, and delivery of IT projects. Ensure quality assurance processes are in place to meet business needs and drive growth. |
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
Why people choose us for their career
Loading reviews...
Frequently Asked Questions
Debug: False
Course fee
MOST POPULAR
Fast Track
GBP £149
Complete in 1 month
Accelerated Learning Path
- 3-4 hours per week
- Early certificate delivery
- Open enrollment - start anytime
Standard Mode
GBP £99
Complete in 2 months
Flexible Learning Pace
- 2-3 hours per week
- Regular certificate delivery
- Open enrollment - start anytime
What's included in both plans:
- Full course access
- Digital certificate
- Course materials
All-Inclusive Pricing • No hidden fees or additional costs
Get course information
Earn a career certificate
CERTIFIED PROFESSIONAL IN AI-DRIVEN QUALITY ASSURANCE
is awarded to
Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
