With the current trends in technology and the growing incorporation of Artificial Intelligence and Machine Learning, Artificial Intelligence United (AiU) was created to support the understanding of implementation of these important advancements. AiU - Certified Tester in Artificial Intelligence (AiU-CTAI) is a 3-day practical certification course, which goes beyond the fundamentals of Artificial Intelligence and Machine Learning, to discover the differences associated with testing in this new world. Join the Artificial Intelligence United community and embark on your AI/ML journey today.

Target Audience

Anybody who has to test or manage testing of systems that involve AI.


In general, there are no prerequisites for attending an AiU course or taking the exam. We do recommend you hold the ISTQB CTFL certificate and have first hand experience in the field of AI & ML.

Course Duration

3 days

Course Outline:

Chapter 1 - Introduction to Artificial Intelligence

1.1 Artificial Intelligence (AI) and Machine Learning (ML)
1.1.1 Defining Artificial Intelligence (AI)
1.1.2 Types of AI
1.2 Types of ML
1.2.1 Supervised learning - Classification and Regression
1.2.2 Unsupervised learning - Clustering and Association
1.2.3 Reinforced Learning
1.2.4 Deep Learning (DL) and types of Neural Networks (RNN, DNN, CNN)
1.3 Stages of ML Process
1.3.1 Stages of ML Process - CRISP-DM process
1.3.2 Steps for Identification of ML problem type

Chapter 2 - Overview of Testing AI Systems

2.1.1 Offline and Online testing of AI Systems
2.2.1 AI testing Vs traditional testing
2.3.1 Quality characteristics for evaluating AI systems
2.3.2 Extended Quality characteristics specific to AI

Chapter 3 - Offline Testing of AI Systems

3.1 Data preparation and preprocessing
3.1.1 Steps of data preparation and preprocessing
3.1.2 Data manipulation and filtering
3.1.3 Processing of Unstructured data (images)
3.1.4 Processing of Unstructured data (text)
3.1.5 Dimensionality Reduction
3.1.6 Data visualization
3.1.7 Anomaly/Outliers detection
31.8 Outliers Detection Techniques
3.1.9 Data imputation
3.2 Metrics
3.2.1 Role of metrics
3.2.2 Metrics for supervised and unsupervised learning
3.2.3 Inertia and adjusted rand score
3.2.4 Support, confidence and lift
3.2.5 Confusion matrix
3.2.6 Accuracy, precision, recall, specificity and F1-score
3.2.7 RMSE and R-Square
3.3.1 Training, validation and testing datasets
3.3.2 Underfitting and Overfitting
3.3.3 Cross-validation
3.4.1 Analytics

Chapter 4 - Online Testing of AI Systems

4.1 Architecture of an AI application
4.1.1 Components of an intelligent app and their testing needs
4.1.2 Interaction of AI and non-AI parts
4.2 Linguistic analysis method
4.2.1 Linguistic analysis based test design
4.3 Testing AI systems
4.3.1 Test a chatbot
4.3.2 Testing an image-classifier

Chapter 5 - Explainable AI

5.1.1 Explainable AI and its need
5.1.2 LIME
5.1.3 CAM for Neural Networks
5.1.4 Counterfactual examples

Chapter 6 - Risks and Test Strategy for AI Systems

6.1 Risks in testing AI
6.1.1 Challenges of testing AI systems
6.1.2 Risk of using pre-trained models
6.1.3 Risk of Concept Drift (CD)
6.1.4 Challenges of AI test environment
6.2 Test Strategy
6.2.1 Test Strategy for Testing AI applications

Chapter 7 - AI in Testing

7.1 AI for STLC
7.1.1 AI for Software Testing Lifecycle methods
7.1.2 AI for smart reporting and dashboards
7.2 AI based automation tools
7.2.1 Tools

Mode: Hands On, Written Exercises, Group Discussion, Case Study etc

For queries contact: Vinay at +91 9910105147 / vinay@veritysoftware.in