Learn Python functions in Lecture 6 with simple explanations and practical examples. Understand def, parameters, arguments, return values, *args, **kwargs, and real Data Science use-cases. Includes MCQs and practice tasks. What You Will Learn in This Lecture By the end,…
Explore Intelligent Software Testing fundamentals: AI-driven test generation, automated oracle solutions, and ML-based test prioritization for efficient QA. Software testing is a crucial part of the SDLC, ensuring quality, reliability, and correctness. Modern intelligent systems leverage AI and ML to…
Learn the fundamentals of expository writing, its purpose, structure, and importance in academic and professional communication. This complete lecture explains clarity, coherence, neutrality, and how exposition differs from creative writing. Introduction to Expository Writing Expository writing is one of the…
Lecture 10 provides a complete hands-on guide to Data Mining implementation using Python with Pandas, NumPy, Matplotlib, Scikit-learn, and MLxtend. Includes preprocessing, classification, clustering, association rule mining, examples, diagrams, code, and evaluation techniques. Python has become the most widely used…
Learn loops in Python with clear examples for beginners. Lecture 5 covers for loops, while loops, range(), nested loops, and real Data Science use-cases, complete with MCQs and practice questions. What You Will Learn By the end of this lecture,…
Lecture 9 explores current Data Mining trends and future research frontiers, including big data, AI integration, IoT, cloud analytics, privacy-preserving models, multimodal data, quantum computing, and modern industry applications. Data Mining is no longer limited to simple classification, clustering, or…