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.