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ISTQB AI Tester Training

AI training for testers can equip them with the knowledge and skills to effectively test AI-powered systems and leverage AI to enhance the testing process. This includes understanding AI concepts, testing methodologies, and using AI-powered tools. 

This includes understanding AI concepts, testing methodologies, challenges of Testing an AI Based system and using AI-powered tools. 

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Course Includes

  • 32 of Sessions
  • 24/7 Lifetime Support
  • One-on-one doubt clearing
  • 4 of Labs
  • Certification Based Curriculum
  • Career path guidance
  • Real-time Use cases
  • Flexible Schedules

Course Overview

  • ISTQB AI Testing certification is a career must-have for everyone who may test AI-based systems or is using AI for testing.You need to future-proof your career and your organization with AI Testing certification. ISTQB AI Testing certification is critical for the careers of not just testers, but also test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers.
  • This certification is also important for anyone who wants a basic understanding of testing AI-based systems or will be using AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants.

Participants must be ISTQB Foundation level certified for ISTQB AI Certification

What is your return policy?

We offer a 30-day return policy from the date of purchase.

Do I need a receipt to return an item?

Yes, a valid receipt or proof of purchase is required.

Can I return online purchases in store?

Yes, online purchases can be returned in any of our physical stores.

How long does shipping take?

Standard shipping typically takes 3–5 business days.

Do you ship internationally?

Yes, we ship to most countries worldwide. Additional charges may apply.

Which countries are excluded?

We currently do not ship to embargoed countries or regions with postal restrictions.

Chapter 1 - Introduction to AI

Definition of AI and AI Effect
Narrow, General and Super AI
AI-Based and Conventional Systems
AI Technologies
AI Development Frameworks
Hardware for AI-Based Systems
AI as a Service (AIaaS)
Contracts for AI as a Service
AIaaS Examples
Pre-Trained Models
Introduction to Pre-Trained Models
Transfer Learning
Risks of using Pre-Trained Models and Transfer Learning
Standards, Regulations and AI
Chapter 2 - Quality Characteristics for AI-Based Systems

Flexibility and Adaptability
Autonomy
Evolution
Bias
Ethics
Side Effects and Reward Hacking
Transparency, Interpretability and Explainability
Safety and AI
Chapter 3 - Machine Learning (ML) – Overview

Forms of ML
Supervised Learning
Unsupervised Learning
Reinforcement Learning
ML Workflow
Selecting a Form of ML
Factors Involved in ML Algorithm Selection
Overfitting and Underfitting
Overfitting
Underfitting
Chapter 4 - ML Data

Data Preparation as Part of the ML Workflow
Challenges in Data Preparation
Hands-On Exercise: Data Preparation for ML
Training, Validation and Test Datasets in the ML Workflow
Dataset Quality Issues
Data Quality and its Effect on the ML Model
Data Labelling for Supervised Learning
Approaches to Data Labelling
Mislabeled Data in Datasets
Chapter 5 - ML Functional Performance Metrics

Confusion Matrix
Additional ML Functional Performance Metrics for Classification, Regression and Clustering
Limitations of ML Functional Performance Metrics
Selecting ML Functional Performance Metrics
Benchmark Suites for ML
Chapter 6 - ML Neural Networks and Testing

Neural Networks
Coverage Measures for Neural Networks
Chapter 7 - Testing AI-Based Systems Overview

Specification of AI-Based Systems
Test Levels for AI-Based Systems
Input Data Testing
ML Model Testing
Component Testing
Component Integration Testing
System Testing
Acceptance Testing
Test Data for Testing AI-based Systems
Testing for Automation Bias in AI-Based Systems
Documenting an AI Component
Testing for Concept Drift
Selecting a Test Approach for an ML System
Chapter 8 - Testing AI-Specific Quality Characteristics;

Challenges Testing Self-Learning Systems
Testing Autonomous AI-Based Systems
Testing for Algorithmic, Sample and Inappropriate Bias
Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
Challenges Testing Complex AI-Based Systems
Testing the Transparency, Interpretability and Explainability of AI-Based Systems
Test Oracles for AI-Based Systems
Test Objectives and Acceptance Criteria
Chapter 9 - Methods and Techniques for the Testing of AI-Based Systems

Adversarial Attacks and Data Poisoning
Adversarial Attacks
Data Poisoning
Pairwise Testing
Hands-On Exercise: Pairwise Testing
Back-to-Back Testing
A/B Testing
Metamorphic Testing (MT)
Experience-Based Testing of AI-Based Systems
Selecting Test Techniques for AI-Based Systems
Chapter 10 - Test Environments for AI-Based Systems

Test Environments for AI-Based Systems
Virtual Test Environments for Testing AI-Based Systems
Chapter 11 - Using AI for Testing

AI Technologies for Testing
Hands-On Exercise: The Use of AI in Testing
Using AI to Analyze Reported Defects
Using AI for Test Case Generation
Using AI for the Optimization of Regression Test Suites
Using AI for Defect Prediction
Hands-On Exercise: Build a Defect Prediction System
Using AI for Testing User Interfaces
Using AI to Test Through the Graphical User Interface (GUI)
Using AI to Test the GUI
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