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所在平台: Udemy |
课程主页: https://www.udemy.com/course/istqb-ai-testing-certification/
课程评论：没有评论
**Coursera 课程总结：ISTQB AI 测试 - 完整培训及考前准备** 本课程是 ISTQB 认证测试 AI 测试的完整培训计划，旨在帮助专业人士有效理解、测试并认证其在基于 AI 的系统方面的专业知识。课程紧密遵循官方 ISTQB AI 测试教学大纲，提供结构化且全面的方法，涵盖 AI 基础知识、机器学习概念、质量特性以及高级 AI 测试方法。 **课程亮点：** * **章节测验：** 强化核心学习目标。 * **实践练习与场景题：** 有效应用课堂概念。 * **全面模拟考试：** 模拟 ISTQB AI 测试认证考试体验。 * **职业发展建议：** 认证后提升个人资料和职业机会的实用技巧。 **课程大纲：** 1. **AI 导论：** 理解 AI 概念、类型及实际应用。 2. **AI 系统质量特性：** 探索 AI 特有的属性，如透明度、公平性、鲁棒性和伦理。 3. **机器学习 (ML) 概述：** 掌握 ML 基础，包括监督学习和无监督学习。 4. **ML 数据：** 深入学习数据预处理、特征工程、数据集管理和质量保证。 5. **ML 功能性性能指标：** 学习衡量模型有效性的关键评估指标。 6. **ML 神经网络与测试：** 深入了解深度学习原理、神经网络结构及相关测试技术。 7. **AI 系统测试概述：** 理解测试 AI 应用的独特挑战和策略。 8. **AI 特有质量特性测试：** 重点关注可解释性、易理解性、偏差识别和安全测试。 9. **AI 测试方法和技术：** 探索行之有效的测试策略，包括成对测试、探索性测试和白盒测试。 10. **AI 系统测试环境：** 了解适用于测试复杂 AI 系统的自动化工具和环境。 11. **利用 AI 进行测试：** 运用 AI 技术来增强传统软件测试实践。 12. **认证后提升职业档案的技巧。** **适用人群：** QA 专业人士、软件测试人员、测试经理、转向 AI 测试的开发人员以及目标获得 ISTQB AI 测试认证的 AI 从业人员。 通过本课程，您可以获得专家知识、实践见解和以认证为导向的准备，从而加速您的职业发展。
This ISTQB Certified Tester AI Testing course is a complete training program designed to help professionals effectively understand, test, and certify their expertise in AI-based systems. Aligned closely with the official ISTQB syllabus, this course provides a structured and comprehensive approach covering AI fundamentals, machine learning concepts, quality characteristics, and advanced AI testing methodologies.Course Highlights:Chapter-wise quizzes to reinforce key learning objectives.Practical exercises and scenario-based questions to apply concepts effectively.A comprehensive mock exam simulating the ISTQB AI Testing certification experience.Proven tips and guidance on enhancing your professional profile and career opportunities post-certification.Course Outline:Chapter 1: Introduction to AI - Understand AI concepts, types, and practical applications.Chapter 2: Quality Characteristics for AI-Based Systems - Explore AI-specific attributes like transparency, fairness, robustness, and ethics.Chapter 3: Machine Learning (ML) Overview - Master ML fundamentals including supervised and unsupervised learning.Chapter 4: ML Data - Delve into data preprocessing, feature engineering, dataset management, and quality assurance.Chapter 5: ML Functional Performance Metrics - Learn key evaluation metrics to measure model effectiveness.Chapter 6: ML Neural Networks and Testing - Gain insights into deep learning principles, neural network structures, and relevant testing techniques.Chapter 7: Testing AI-Based Systems Overview - Understand unique challenges and strategies in testing AI applications.Chapter 8: Testing AI-Specific Quality Characteristics - Special focus on explainability, interpretability, bias identification, and safety testing.Chapter 9: Methods and Techniques for AI Testing - Explore proven testing strategies including pairwise, exploratory, and white-box testing.Chapter 10: Test Environments for AI-Based Systems - Discover the automation tools and environments suitable for testing complex AI systems.Chapter 11: Using AI for Testing - Leverage AI technologies to enhance traditional software testing practices.Tips on enhancing your profile post certificationThis course is ideal for QA professionals, software testers, test managers, developers transitioning into AI testing, and AI practitioners aiming for ISTQB AI Testing certification. Enroll now and accelerate your career with expert knowledge, practical insights, and certification-focused preparation.
