Foundations of Testing Machine Learning and AI Models

As artificial intelligence and large language models become integrated into everyday software, ensuring their reliability, safety, and accuracy is more critical than ever. Traditional software testing methods fall short when applied to the probabilistic nature of modern AI. This course provides a clear, step-by-step introduction to evaluating machine learning models, foundational LLMs, and conversational agents, giving you the skills to design robust testing strategies. What you'll learn: - Understand the foundational differences between testing traditional software and evaluating probabilistic AI systems. - Apply data-splitting techniques like K-Fold cross-validation to prevent overfitting and ensure reliable model generalization. - Evaluate Large Language Models (LLMs) using industry-standard benchmarks such as MMLU, HumanEval, and BLEU. - Assess chatbot and conversational AI performance for accuracy, safety, and coherence. - Implement modern testing patterns for Retrieval-Augmented Generation (RAG) systems, verifying both retrieval accuracy and response generation. - Identify and test for ethical risks, bias, toxicity, and security vulnerabilities like prompt injection. You will start with core testing terminology and foundational machine learning concepts before progressing to advanced evaluation metrics for generative AI. Through clear written explanations and practical conceptual exercises, you will learn how to design end-to-end testing pipelines. This course is designed for beginner QA engineers, developers, and AI enthusiasts who want to transition into AI safety and evaluation. No advanced mathematical background or programming experience is required. Start building safer, more reliable AI systems today.