Udemy - Exam Preparation - ISTQB CT-AI

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.7 GB
  • Uploaded Byfreecoursewb
  • Downloads29
  • Last checkedMay. 03rd '26
  • Date uploadedMay. 02nd '26
  • Seeders 5
  • Leechers9

Infohash : 80DC1364D341736EB3866A84B13C166070899891

Exam Preparation: ISTQB CT-AI

https://WebToolTip.com

Published 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 11m | Size: 1.68 GB

Prepare for ISTQB CT-AI exam: AI testing, machine learning, metrics, techniques & practice questions

What you'll learn
Understand AI fundamentals and AI-based systems
Identify key quality aspects like bias and ethics
Learn core machine learning concepts and workflows
Understand data preparation and dataset types
Use ML metrics like confusion matrix
Learn how to test AI-based systems
Handle challenges like bias and concept drift
Explore AI testing techniques and methods
Use AI to support testing activities
Prepare for the ISTQB CT-AI exam

Requirements
Basic understanding of software testing concepts
Familiarity with IT fundamentals
Willingness to learn and prepare for the CT-AI exam

Files:

[ WebToolTip.com ] Udemy - Exam Preparation - ISTQB CT-AI
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1. About instructor.mp4 (3.6 MB)
    • 2. Information about Exam CT‑AI.mp4 (24.6 MB)
    10 - Methods and Techniques for the Testing of AI-Based Systems
    • 51. Testing for Algorithmic, Sample and Inappropriate Bias.mp4 (20.4 MB)
    • 52. Adversarial Attacks and Data Poisoning.mp4 (38.7 MB)
    • 53. Pairwise Testing.mp4 (24.8 MB)
    • 54. Back-to-Back Testing.mp4 (19.1 MB)
    • 55. A B Testing.mp4 (13.1 MB)
    • 56. Metamorphic Testing (MT).mp4 (27.2 MB)
    • 57. Experience-Based Testing of AI-Based Systems.mp4 (28.6 MB)
    • 58. Selecting Test Techniques for AI-Based Systems.mp4 (28.2 MB)
    11 - Test Environments for AI-Based Systems
    • 59. Test Environments for AI-Based Systems.mp4 (11.1 MB)
    • 60. Virtual Test Environments for Testing AI-Based Systems.mp4 (33.3 MB)
    12 - Using AI for Testing
    • 61. AI Technologies for Testing.mp4 (21.8 MB)
    • 62. Using AI to Analyze Reported Defects.mp4 (19.4 MB)
    • 63. Using AI for Test Case Generation.mp4 (16.3 MB)
    • 64. Using AI for the Optimization of Regression Test Suites.mp4 (14.2 MB)
    • 65. Using AI for Defect Prediction.mp4 (13.8 MB)
    • 66. Using AI to Test the GUI.mp4 (23.2 MB)
    14 - Sample exam
    • 1. Exam CT-AI.html (77.1 KB)
    2 - Introduction to AI
    • 10. Pre-Trained Models.mp4 (38.1 MB)
    • 11. Standards, Regulations and AI.mp4 (31.6 MB)
    • 3. Definition of AI and AI Effect.mp4 (29.1 MB)
    • 4. Narrow, General and Super AI.mp4 (28.5 MB)
    • 5. AI-Based and Conventional Systems.mp4 (12.4 MB)
    • 6. AI Technologies.mp4 (27.6 MB)
    • 7. AI Development Frameworks.mp4 (15.9 MB)
    • 8. Hardware for AI-Based Systems.mp4 (32.1 MB)
    • 9. AI as a Service (AIaaS).mp4 (13.2 MB)
    3 - Quality Characteristics for AI-Based Systems
    • 12. Flexibility and Adaptability.mp4 (41.2 MB)
    • 13. Autonomy.mp4 (27.5 MB)
    • 14. Evolution.mp4 (12.2 MB)
    • 15. Bias.mp4 (35.7 MB)
    • 16. Ethics.mp4 (26.3 MB)
    • 17. Side Effects and Reward Hacking.mp4 (28.2 MB)
    • 18. Transparency, Interpretability and Explainability.mp4 (15.3 MB)
    • 19. Safety and AI.mp4 (23.8 MB)
    4 - Machine Learning (ML) – Overview
    • 20. Forms of ML.mp4 (19.9 MB)
    • 21. ML Workflow.mp4 (48.0 MB)
    • 22. Selecting a Form of ML.mp4 (30.0 MB)
    • 23. Factors Involved in ML Algorithm Selection.mp4 (16.1 MB)
    • 24. Overfitting and Underfitting.mp4 (29.2 MB)
    5 - ML - Data
    • 25. Data Preparation as Part of the ML Workflow.mp4 (46.4 MB)
    • 26. Challenges in Data Preparation.mp4 (28.4 MB)
    • 27. Training, Validation and Test Datasets in the ML Workflow.mp4 (35.0 MB)
    • 28. Dataset Quality Issues.mp4 (41.3 MB)
    • 29. Data Quality and its Effect on the ML Model.mp4 (28.0 MB)
    • 30. Data Labelling for Supervised Learning.mp4 (37.8 MB)
    6 - ML Functional Performance Metrics
    • 31. Confusion Matrix.mp4 (36.1 MB)
    • 32. ML Performance Metrics (Classification, Regression, Clustering).mp4 (29.1 MB)
    • 33. Limitations of ML Functional Performance Metrics.mp4 (14.0 MB)
    • 34. Selecting ML Functional Performance Metrics.mp4 (25.1 MB)
    • 35. Benchmark Suites for ML.mp4 (26.3 MB)
    7 - ML - Neural Networks and Testing
    • 36. Neural Networks.mp4 (31.8 MB)
    • 37. Coverage Measures for Neural Networks.mp4 (30.5 MB)
    8 - Testing AI-Based Systems Overview
    • 38. Specification of AI-Based Systems.mp4 (25.9 MB)
    • 39. Test Levels for AI-Based Systems.mp4 (43.1 MB)
    • 40. Test Data for Testing AI-based Systems.mp4 (27.6 MB)
    • 41. Testing for Automation Bias in AI-Based Systems.mp4 (12.5 MB)
    • 42. Documenting an AI Component.mp4 (35.3 MB)
    • 43. Testing for Concept Drift.mp4 (27.8 MB)
    • 44. Selecting a Test Approach for an ML System.mp4 (29.6 MB)
    9 - Testing AI-Specific Quality Characteristics
    • 45. Challenges Testing Self-Learning Systems.mp4 (27.2 MB)
    • 46. Testing Autonomous AI-Based Systems.mp4 (16.0 MB)
    • 47. Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems.mp4 (28.5 MB)
    • 48. Testing Transparency, Interpretability and Explainability of AI Systems.mp4 (24.8 MB)
    • 49. Test Oracles for AI-Based Systems.mp4 (22.6 MB)
    • 50. Test Objectives and Acceptance Criteria.mp4 (30.6 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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