Explore the fundamentals of machine learning with a focus on k-nearest neighbours in this comprehensive lecture. Delve into classification and regression techniques, examining real-world examples to understand their applications. Learn about consistent hypotheses and the concept of nearest neighbour algorithms. Discover how to implement and evaluate k-nearest neighbour models, including accuracy assessment and addressing underfitting issues. Gain valuable insights into this essential machine learning technique and its practical implications in data analysis and prediction tasks.