Istqb Certified Tester Ai Testing (Ct-Ai) Complete Training

Master AI Testing: Complete ISTQB CT-AI Certification Training

What you'll learn

Stay Ahead of AI Trends: Discover how AI advancements are reshaping testing, and equip yourself to work with cutting-edge tools and methodologies.

Build & Test with Confidence: Gain hands-on experience with machine learning models, learning how to test effectively to boost quality and performance.

Master AI Testing Challenges:Learn strategies for managing AI's unique challenges like bias, ethics & non-determinism,to ensure trustworthy & transparent system

Enhance Testing with AI Tools: Explore how AI can automate and optimize software testing, creating faster and smarter workflows for your team.

Requirements

To gain this certification, candidates must hold the Certified Tester Foundation Level certificate.

Description

This comprehensive course is aligned with the ISTQB syllabus for AI Testing certification, providing you with the foundational knowledge and practical skills required to achieve ISTQB Certified Tester status in AI Testing. Designed to ensure international consistency, the syllabus offers a structured approach to learning AI-based system testing, focusing on the unique challenges posed by artificial intelligence and machine learning technologies.The course content is tailored to cover the key concepts, terminology, and best practices in AI testing, with detailed instructional objectives and hands-on learning outcomes for each knowledge area. Participants will gain insights into how AI systems function, the intricacies of machine learning models, and effective testing techniques to ensure quality, performance, and reliability in AI-driven systems.This structured format ensures a deep dive into both theoretical concepts and practical applications of AI testing. Each chapter builds progressively to provide a holistic understanding of AI systems, their quality attributes, and the most effective testing methodologies.What You'll Learn:The basic concepts of AI and machine learning, with a special focus on testing techniques.How to evaluate data quality, functional performance, and neural network behavior.Practical approaches to testing AI-specific quality characteristics like bias, transparency, and robustness.Advanced techniques and tools for creating effective test environments for AI systems.Leveraging AI technologies for enhancing traditional testing processes, including defect analysis and regression suite optimization.By the end of this course, you’ll have the skills and knowledge required to confidently tackle AI system testing challenges and earn your ISTQB Certified Tester certification in AI Testing.

Overview

Section 1: Overview

Lecture 1 Course Overview

Section 2: Module 1 : Introduction to AI

Lecture 2 Definition of AI

Lecture 3 AI Technologies and Frameworks

Lecture 4 AI as a Service (AIaaS) and Pretrained Models

Lecture 5 Standards, Regulations, and AI

Section 3: Module 2 : Quality Characteristics for AI-Based Systems

Lecture 6 Flexibility, Adaptability and Autonomy in AI

Lecture 7 Evolution in AI Systems, Bias in AI, and Ethics in AI

Lecture 8 AI Risks, Transparency, and Safety

Section 4: Module 3 : Machine Learning - Overview

Lecture 9 Forms of Machine Learning

Lecture 10 Machine learning Workflow

Lecture 11 Selecting a Form of Machine Learning

Lecture 12 Overfitting and Underfitting

Section 5: Module 4 : Machine Learning (ML) – Data

Lecture 13 Data Preparation as part of the ML Workflow

Lecture 14 Training, Validation & Test DS in ML Workflow

Lecture 15 Dataset Quality Issues

Lecture 16 Data Quality and its Effect on ML

Section 6: Module 5 : ML Functional Performance Metrics

Lecture 17 Confusion Matrix

Lecture 18 ROC, AUC and R squared

Lecture 19 Evaluating Machine Learning Models: Metrics for Clustering and Beyond

Lecture 20 Benchmark Suites for ML

Section 7: Module 6 : ML - Neural Networks and Testing

Lecture 21 Neural Networks

Lecture 22 Coverage measures for Neural Networks- Neuron, Threshold & Sign Change coverage

Lecture 23 Value Change, Sign Sign and Nearest Neighbour coverage

Lecture 24 Testing Neural Network : Tools and Frameworks

Section 8: Module 7 : Testing AI based systems - Overview

Lecture 25 Specifications of AI based systems

Lecture 26 Testing levels of AI based systems

Lecture 27 Challenges for testing AI based system

Lecture 28 Selecting a Test Approach for an ML System

Section 9: Module 8: Testing AI-Specific Quality Characteristics

Lecture 29 AI-Specific Quality Characteristics

Lecture 30 Challenges in Testing these systems & Strategy

Lecture 31 Test Objectives and Acceptance Criteria

Section 10: Module 9 : Methods and Techniques for the Testing of AI-Based Systems

Lecture 32 Adversarial Attacks and Data Poisoning

Lecture 33 Pairwise testing

Lecture 34 Back to Back testing

Lecture 35 A/B Testing

Lecture 36 Metamorphic Testing (MT)

Lecture 37 Experience based Testing for AI systems

Lecture 38 Selecting Test Techniques for AI-Based Systems

Section 11: Module 10 : Test Environments for AI-Based Systems

Lecture 39 Test Environments for AI-Based Systems

Section 12: Module 11 : Using AI for testing

Lecture 40 AI technologies for Testing

Lecture 41 Uses of AI in Testing

Lecture 42 Using AI for Testing User Interface

This course are for people in roles as testers, test analysts, data analysts, test engineers, test consultants/managers, UAT testers and software developers.,This course is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants.,This course is an complete guide and aligned with ISTQB's syllabus to prepare for the Certified Tester AI Testing (CT-AI) exams