- Test Specialist
- Partner training
- Bookable as in-house seminar
Termin / Ort
01.09.2020 in Toronto/ Mississauga, ON imbus AG Standort Toronto/ Mississauga, ON Kanada
2,249.10 C$ *inkl. 10% Frühbucherrabatt Termingarantie
Prüfung (Teilnahme optional)
300.00 C$ *
* zzgl. 13.00% ges. MwSt.
Go beyond the fundamentals of AI and machine learning, to delve the differences associated with testing in this new environment and attest your qualification with your certification.
The course provides a good introduction and overview of artificial intelligence methods used nowadays, starting from basic definitions to the different forms of AI model testing, online as well as offline. The particularities of risks, quality attributes and strategies for testing AI applications are outlined. In the last part it is demonstrated how AI is making testing tools smarter. Following this course will lead to a broad understanding of the topic.
At the end of this course, the participant will be able to:
- Understand current trends, industry applications of Artificial Intelligence (AI) using Machine Learning (ML).
- Compare different implemented ML algorithms to help choose the most suitable one.
- Evaluate models for both supervised and unsupervised learning.
- Design and execute test cases for AI systems.
- Use various methods for bringing transparency into model workings.
- Define a test strategy for testing of AI systems.
- Understand where AI can be used in manual testing and in test automation.
- Use AI based test execution tools to automate tests
- 1.7 The Selenium Toolset
On top of that, if you pass the exam, you will hold the “AiU Certified Tester in AI” certificate.
Candidates should have a general knowledge of basic programming and some knowledge of Python.
The course is structured according to the AiU Certified Tester in AI syllabus. This way you can relate the topics covered in the course to the syllabus.
- Introduction to Artificial Intelligence: introducing artificial intelligence (AI), Machine Learning (ML) and Deep Learning (DL).
- Overview of testing AI systems: off-line and online testing of AI applications, data preparation and pre-processing (outlier detection, dimension reduction), imputation and visualization.
- Metrics for supervised (Accuracy, Precision, Recall/sensitivity, Specificity and F1-score) and unsupervised learning (Inertia and Rand score, Support, Confidence and Lift metrics) to find the best AI model.
- Explainable AI: examination and evaluation of complex (DL models) models by varying input variables and observing variations in outcomes while constructing a simple interpretable model.
- Risks and test strategy for AI systems.
- AI in testing: application of AI in the test process itself, smart dashboards and test automation tools.
- Erfahrene Softwaretester
- technisch interessierte Anwender
- Interessenten an der Zertifizierung