Big O notation classifies algorithms based on their efficiency as input size increases. It helps in understanding time and space complexity through examples like linear, constant, and exponential growth. The course covers various complexities, including O(n), O(n^2), and O(log n), using practical coding illustrations and recursive functions to solidify concepts.