Algorithm

A recipe is an algorithm. "Preheat oven to 350. Mix flour, sugar, eggs. Pour into pan. Bake for 30 minutes." A set of clear steps that, if followed correctly, produce a predictable result. Algorithms are the same thing, but for computers.

Every piece of software you use runs on algorithms. When Google ranks search results, there's an algorithm deciding what goes first. When Uber matches you with a driver, there's an algorithm figuring out who's closest. When your bank checks whether a transaction is fraudulent, there's an algorithm analyzing the pattern. They're the invisible instructions running the digital world.

The word sounds intimidating, but the concept is simple: it's just a procedure. A series of steps to get from input to output. Some algorithms are simple (sort these numbers from smallest to largest). Some are mind-bendingly complex (predict what this person wants to buy next). But they're all just... instructions.

Because algorithms shape your reality more than you think. They decide what news you see, what products get recommended to you, whether your resume gets seen by a human, and what interest rate you're offered. They're not magic — they're rules written by people. Understanding that helps you question them when the results seem off instead of just accepting "the computer said so."

An algorithm is a finite sequence of well-defined instructions for solving a computational problem. They're analyzed by time complexity (Big O notation) and space complexity. Classic categories include sorting (quicksort, mergesort), searching (binary search, BFS/DFS), dynamic programming, greedy algorithms, and graph algorithms. In ML, algorithms include gradient descent, backpropagation, and various optimization methods (Adam, SGD). Algorithm design involves trade-offs between speed, memory, accuracy, and interpretability.