Complete ISTQB AI Testing Course: Full Exam Prep, Certification Training and a Sample Exam

What you'll learn

Understand fundamental AI concepts, history, and real-world applications.
Learn key quality attributes such as adaptability, transparency, and performance in AI systems.
Explore different ML types, workflows, and considerations for selecting ML models.
Understand data preprocessing, bias, quality challenges, and data handling in AI systems.
Learn evaluation metrics like accuracy, precision, recall, and F1-score for ML models.
Gain insights into neural networks, coverage measures, and challenges in testing deep learning models.
Examine key test levels, risks, and methodologies for validating AI systems.
Explore AI-specific testing techniques focusing on bias, explainability, and robustness.
Learn various testing methods, including pairwise testing, metamorphic testing, and back-to-back testing.
Understand the infrastructure and tools required for AI system testing.
Discover how AI can automate and enhance software testing techniques.

Requirements

No programming experience required. Detailed guides provided for everything you need to know

Description

This ISTQB Certified Tester AI Testing course is a complete training program designed to help professionals understand and test AI-based systems effectively. Covering the ISTQB syllabus, this course provides a structured approach to AI fundamentals, machine learning, quality characteristics, and AI testing methodologies.Course Outline:Chapter 1: Introduction to AI – Understand AI concepts, types, and applications.Chapter 2: Quality Characteristics for AI-Based Systems – Explore AI-specific attributes like transparency, fairness, and robustness.Chapter 3: Machine Learning (ML)

Overview

– Learn ML fundamentals, supervised and unsupervised learning.Chapter 4: ML Data – Understand data preprocessing, feature engineering, and dataset quality.Chapter 5: ML Functional Performance Metrics – Explore key evaluation techniques.Chapter 6: ML Neural Networks and Testing – Learn about deep learning and neural networks.Chapter 7: Testing AI-Based Systems

Overview

– Understand AI testing challenges.Chapter 8: Testing AI-Specific Quality Characteristics – Focus on explainability, bias, and safety testing.Chapter 9: Methods and Techniques for AI Testing – Learn testing strategies.Chapter 10: Test Environments for AI-Based Systems – Explore test automation and tools.Chapter 11: Using AI for Testing – Learn how AI can enhance software testing.This course is ideal for QA professionals, testers, and AI practitioners preparing for the ISTQB AI Testing certification with practical examples and exam-focused insights.

Who this course is for

This course is designed for professionals working with AI-based systems and AI-driven testing, including but not limited to: Testing & QA Professionals – Testers, test analysts, test engineers, test consultants, test managers, and user acceptance testers. Data & AI Specialists – Data analysts and professionals working with AI models. Software Development Teams – Developers, software engineers, and technical architects involved in AI testing. Project & Quality Managers – Business analysts, quality managers, and project managers looking to understand AI testing methodologies. IT & Operations Leaders – IT directors, operations team members, and management consultants involved in AI adoption. Prerequisite: Candidates must hold the Certified Tester Foundation Level (CTFL) certification to qualify for this course. This course is ideal for anyone looking to enhance their expertise in testing AI-based systems and leveraging AI for software testing.
Homepage:

https://www.udemy.com/course/istqb-ai-testing-certification/