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Time complexity is a way of expressing the amount of time an algorithm takes to complete as a function of the size of its input. Two common types of time complexities are linear time (O(n)) and constant time (O(1)).

Linear Time (O(n)):

Linear time complexity means that the algorithm's running time grows linearly with the size of the input.
If an algorithm has a linear time complexity of O(n), it means that as the input size (n) increases, the time it takes for the algorithm to complete will increase proportionally.
Example: A simple linear search algorithm that goes through each element in a list until it finds the desired element. The time it takes is directly proportional to the size of the list.
Constant Time (O(1)):

Constant time complexity means that the algorithm's running time does not depend on the size of the input; it remains constant regardless of the input size.
This is considered highly efficient because the algorithm's execution time is the same, regardless of how large the input is.
Example: Accessing an element in an array by its index. It doesn't matter if the array has 10 elements or 1,000; accessing a specific index takes the same amount of time.