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1. Introduction to AI
16 Lessons-
Preview1. Introduction to AI
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Start1.1 Definition of AI and AI Effect
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Start1.2 Narrow, General and Super AI
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Start1.3 AI-Based and Conventional Systems
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Start1.4 AI Technologies
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Start1.5 AI Development Frameworks
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Start1.6 Hardware for AI-Based Systems
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Start1.7 AI as a Service (AIaaS)
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Start1.7.1 Contracts for AI as a Service
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Start1.7.2 AIaaS Examples
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Start1.8 Pre-Trained Models
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Start1.8.1 Introduction to Pre-Trained Models
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Start1.8.2 Transfer Learning
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Start1.8.3 Risks of using Pre-Trained Models and Transfer Learning
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Start1.9 Standards, Regulations and AI
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StartModule 1 - Resources to download
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2. Quality Characteristics for AI-Based Systems
10 Lessons-
Preview2. Quality Characteristics for AI-Based Systems
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Preview2.1 Flexibility and Adaptability
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Start2.2 Autonomy
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Start2.3 Evolution
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Start2.4 Bias
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Start2.5 Ethics
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Start2.6 Side Effects and Reward Hacking
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Start2.7 Transparency, Interpretability and Explainability
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Start2.8 Safety and AI
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StartModule 2 - Resources to download
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3. Machine Learning (ML) – Overview
12 Lessons-
Preview3. Machine Learning (ML) – Overview
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Start3.1 Forms of ML
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Start3.1.1 Supervised Learning
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Start3.1.2 Unsupervised Learning
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Start3.1.3 Reinforcement Learning
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Start3.2 ML Workflow
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Start3.3 Selecting a Form of ML
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Start3.4 Factors Involved in ML Algorithm Selection
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Start3.5 Overfitting and Underfitting
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Start3.5.1 Overfitting
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Start3.5.2 Underfitting
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StartModule 3 - Resources to download
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4. ML - Data
11 Lessons-
Preview4 ML - Data
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Start4.1 Data Preparation as Part of the ML Workflow
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Start4.1.1 Challenges in Data Preparation
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Start4.2 Training, Validation and Test Datasets in the ML Workflow- Part 1
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Start4.2 Training, Validation and Test Datasets in the ML Workflow- Part 2
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Start4.3 Dataset Quality Issues
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Start4.4 Data Quality and its Effect on the ML Model
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Start4.5 Data Labelling for Supervised Learning
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Start4.5.1 Approaches to Data Labelling
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Start4.5.2 Mislabeled Data in Datasets
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StartModule 4 - Resources to download
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5. ML Functional Performance Metrics
7 Lessons-
Preview5 ML Functional Performance Metrics
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Start5.1 Confusion Matrix
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Start5.2 Additional ML Functional Performance Metrics for Classification, Regression and Clustering
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Start5.3 Limitations of ML Functional Performance Metrics
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Start5.4 Selecting ML Functional Performance Metrics
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Start5.5 Benchmark Suites for ML
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StartModule 5 - Resources to download
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7. Testing AI-Based Systems Overview
15 Lessons-
Preview7. Testing AI-Based Systems Overview
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Start7.1 Specification of AI-Based Systems
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Start7.2 Test Levels for AI-Based Systems
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Start7.2.1 Input Data Testing
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Start7.2.2 ML Model Testing
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Start7.2.3 Component Testing
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Start7.2.4 Component Integration Testing
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Start7.2.5 System Testing
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Start7.2.6 Acceptance Testing
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Start7.3 Test Data for Testing AI-based Systems
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Start7.4 Testing for Automation Bias in AI-Based Systems
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Start7.5 Documenting an AI Component
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Start7.6 Testing for Concept Drift
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Start7.7 Selecting a Test Approach for an ML System
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StartModule 7 - Resources to download
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8. Testing AI-Specific Quality Characteristics
10 Lessons-
Preview8. Testing AI-Specific Quality Characteristics
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Start8.1 Challenges Testing Self-Learning Systems
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Start8.2 Testing Autonomous AI-Based Systems
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Start8.3 Testing for Algorithmic, Sample and Inappropriate Bias
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Start8.4 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
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Start8.5 Challenges Testing Complex AI-Based Systems
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Start8.6 Testing the Transparency, Interpretability and Explainability of AI Based Systems
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Start8.7 Test Oracles for AI-Based Systems
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Start8.8 Test Objectives and Acceptance Criteria
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StartModule 8 - Resources to download
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9. Methods and Techniques for the Testing of AI-Based Systems
11 Lessons-
Preview9. Methods and Techniques for the Testing of AI-Based Systems
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Start9.1 Adversarial Attacks and Data Poisoning
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Start9.1.1 Adversarial Attacks
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Start9.1.2 Data Poisoning
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Start9.2 Pairwise Testing
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Start9.3 Back-to-Back Testing
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Start9.4 A/B Testing
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Start9.5 Metamorphic Testing (MT)
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Start9.6 Experience-Based Testing of AI-Based Systems
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Start9.7 Selecting Test Techniques for AI-Based Systems
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StartModule 9 - Resources to download
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11. Using AI for Testing
10 Lessons-
Preview11. Using AI for Testing
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Start11.1 AI Technologies for Testing
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Start11.2 Using AI to Analyze Reported Defects
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Start11.3 Using AI for Test Case Generation
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Start11.4 Using AI for the Optimization of Regression Test Suites
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Start11.5 Using AI for Defect Prediction
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Start11.6 Using AI for Testing User Interfaces
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Start11.6.1 Using AI to Test Through the Graphical User Interface (GUI)
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Start11.6.2 Using AI to Test the GUI
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StartModule 11 - Resources to download
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ISTQB AI Testing(CT-AI) | AI Testing Masterclass for Testers
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Course Curriculum
Software Testing Mentor and RCV Academy
Manish Verma
Course Pricing
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