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In this class, we will learn what artificial intelligence is, why it matters, and how it’s transforming the world of software testing and development.

In this class, we will learn how AI is defined across different contexts and explore the “AI Effect,” where once-novel AI becomes mainstream and invisible.

In this class, we will learn the differences between Narrow AI, General AI, and Super AI, and understand their capabilities and implications for testing.

In this class, we will learn how AI-based systems differ from conventional rule-based systems, and what that means for testing strategies and quality assurance.

In this class, we will learn about the core technologies that power AI, including machine learning, neural networks, NLP & computer vision and their relevance to testers.

In this class, we will learn about popular AI development frameworks like TensorFlow, PyTorch, and Keras, and how they support the creation and training of AI models.

In this class, we will learn about the specialized hardware, such as GPUs, TPUs, and edge devices which enable efficient AI computation and deployment.

In this class, we will learn how cloud providers offer AI capabilities as services, making it easier to integrate intelligent features into applications without building models from scratch.

In this class, we will learn the key legal and technical considerations when using AIaaS, including service-level agreements, data ownership, and compliance responsibilities.

In this class, we will learn about real-world examples of AIaaS, such as image recognition APIs, chatbot platforms, and predictive analytics tools used across industries.

In this class, we will learn what pre-trained models are, how they work, and why they’re widely used to accelerate AI development and testing.

In this class, we will learn how transfer learning allows AI models to apply existing knowledge to new tasks, improving efficiency and reducing training time.

In this class, we will learn about the potential risks of using pre-trained models and transfer learning, including bias, lack of transparency, and domain mismatch.

In this class, we will learn about the emerging standards, guidelines, and global regulations that ensure AI systems are developed and tested ethically, safely, and responsibly.

Download Module-1 Slides

In this class, we will learn the key quality attributes that define how AI systems behave, adapt, and interact with users and environments, from flexibility and autonomy to ethics and safety.

In this class, we will learn how AI systems adjust to changing inputs, environments, and goals, and why adaptability is crucial for robust performance.

In this class, we will learn what autonomy means in AI systems, how it affects decision-making, and the implications for control and accountability in testing.

In this class, we will learn how AI systems evolve over time through learning and feedback, and how testers can manage and validate continuous change.

In this class, we will learn how bias can enter AI systems through data and design, and how to detect, measure, and mitigate it during testing.

In this class, we will learn the ethical challenges of AI, including fairness, accountability & societal impact and how testers can uphold responsible practices.

In this class, we will learn how AI systems can develop unintended behaviors when optimizing for rewards, and how to test for and prevent these side effects.

In this class, we will learn how to assess whether AI decisions can be understood and explained, and why transparency is vital for trust and compliance.

In this class, we will learn how to evaluate the safety of AI systems, identify potential risks, and ensure they operate reliably under diverse conditions.

Download Module-2 Slides

In this class, we will learn the fundamentals of machine learning, its role in AI systems, and why understanding ML is essential for effective AI testing.

In this class, we will learn the three main forms of machine learning. Supervised, unsupervised, and reinforcement learning and how each impacts testing strategies.

In this class, we will learn how supervised learning uses labeled data to train models and how testers validate predictions and accuracy.

In this class, we will learn how unsupervised learning finds patterns in unlabeled data and what challenges it presents for testers.

In this class, we will learn how reinforcement learning teaches models through rewards and penalties, and how testers evaluate behavior and outcomes.

In this class, we will learn the typical machine learning workflow, from data collection and preprocessing to model training, evaluation, & deployment and where testing fits in.

In this class, we will learn how to choose the right form of machine learning based on the problem type, data availability, and desired outcomes.

In this class, we will learn the key factors that influence algorithm selection, including data size, complexity, interpretability, and performance requirements.

In this class, we will learn how to identify and address overfitting and underfitting, two common issues that affect model accuracy and generalization.

In this class, we will learn what overfitting is, why it happens, and how to test models that perform well on training data but poorly on new data.

In this class, we will learn what underfitting is, how it limits model learning, and how testers detect models that fail to capture meaningful patterns.

Download Module-3 Slides