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Introduction to Software Testing Courses

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<h3 style="margin-bottom: 11px;"><strong>ISTQB Certified Tester Foundation Level AI Testing Training Course Overview</strong></h3> <p style="margin-bottom:11px">ISTQB is a globally recognized certification body for software testing. It is important as it ensures that individuals possess the required knowledge and skills to perform high-quality software testing, following industry standards. The training benefits organizations by enhancing their testing processes, reducing errors, and improving software quality. For individuals, the training provides a comprehensive understanding of testing principles, methodologies, and tools, empowering them to excel in their roles.</p> <p style="margin-bottom:11px">In this course, delegates will gain a comprehensive understanding of testing AI-based systems, including machine learning models, neural networks, and autonomous systems. They will learn about various testing techniques, performance metrics, and challenges in evaluating AI functionality. Delegates will explore the significance of data preparation, test environments, and the impact of AI-specific quality characteristics.</p> <p style="margin-bottom:11px">Course Objectives:</p> <ul style="margin-bottom:11px"> <li>To understand the fundamentals of AI and AI-based systems</li> <li>To explore key AI quality characteristics and their testing challenges</li> <li>To learn about performance metrics for machine learning models</li> <li>To gain insights into testing methodologies for AI-based systems</li> <li>To develop skills in testing neural networks and deep learning systems</li> <li style="margin-bottom:11px">To understand the importance of data preparation in AI testing</li> </ul> <p>After attending this training, delegates will be able to test AI-based systems using the most appropriate testing techniques and tools. They will be able to identify and address AI-specific quality characteristics, ensuring that AI technologies function effectively. Delegates will gain the ability to apply machine learning performance metrics to evaluate system functionality and conduct various types of testing, such as adversarial and pairwise testing.</p>

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