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AiU Certified Tester in AI (CTAI)

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DURATION: 3 Day (40 hours).
Time Division (Break: 15 + 45 + 15 mines).
Course Outcomes:

  • Understand the fundamentals of Data Science and Machine Learning.
  • Analyse and pre-process data proficiently using Python.
  • Apply Supervised Machine Learning techniques for regression and classification.
  • Apply Unsupervised Machine Learning for clustering and natural language.
  • Introduction to Deep learning concepts.


Chapter 1 – Introduction to Artificial Intelligence

  • Artificial Intelligence (AI).

Definition of Artificial Intelligence (AI)

  • Types of AI.
  • Machine Learning (ML).

Definition of ML

  • Supervised Learning – Classification and Regression.
  • Unsupervised Learning – Clustering and Association.
  • Reinforcement Learning.
  • Deep Learning (DL).

Deep Learning and the Types of Neural Networks.

  • Stages of the ML Process.

Chapter 2 – Overview of Testing AI Systems

  • AI Testing Phases.

Offline and Online Testing of AI Systems

  • AI vs. Non-AI Testing.

Testing of AI systems vs. Traditional (non-AI) Systems

  • AI Quality Characteristics.

Quality Characteristics for Evaluating AI Systems

  • Extended Quality Characteristics Specific to AI.

Chapter 3 – Offline Testing of AI Systems

  • Data Preparation and Pre-processing.

Steps of Data Preparation and Pre-processing

  • Data Preparation.
  • Processing of Unstructured Data (Images).
  • Processing of Unstructured Data (Text).
  • Data Imputation.
  • Data Visualization.
  • Anomaly/Outliers Detection.
  • Outliers Detection Techniques.
  • Dimensionality Reduction.
  • Metrics.


Role of Metrics

  • Metrics for Supervised and Unsupervised Learning.
  • Inertia and Adjusted Rand Score.
  • Support, Confidence and Lift metrics.
  • Confusion Matrix.
  • Accuracy, Precision, Recall, Specificity and F1-Score.
  • RMSE and R-Square.
  • Model Evaluation.

Training, Validation and Testing Datasets

  • Under fitting and Overfitting.
  • Cross-validation methods.
  • Analytics.

Types of Analytics


Chapter 4 – Online Testing of AI Systems

  • Architecture of an AI application.

Components of an Intelligent Application and their Testing Needs

  • Interaction of AI and Non-AI Parts.

Linguistic Analysis Test Design Method

  • Linguistic Analysis Test Design Method.

Testing AI Systems.

  • Test a Chatbot.

Chapter 5 – Explainable AI

  • Explainable AI (XAI).

Explainable AI and its Need

  • LIME.
  • CAM for Neural Networks.

Chapter 6 – Risks and Test Strategy for AI Systems

  • Risks in testing AI.

Risks of Testing AI Systems

  • Risk of Using Pre-Trained Models.
  • Risk of Concept Drift (CD).
  • Challenges of AI Test Environment.
  • Test Strategy.

Test Strategy for Testing AI Applications
Chapter 7 – AI in Testing

  • AI for Software Testing Life Cycle (STLC).

AI for STLC Methods

  • AI for Reporting and Smart Dashboards.

AI Based Automation tools

  • Tools.
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AiU Certified Tester in AI (CTAI)

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