Lessons

Learn how arrays and hash maps power everything from feature vectors to embedding lookups in AI systems.

Explore how AI systems break down, search, and manipulate text using string operations and pattern matching.

Discover why sorting and searching algorithms are fundamental to how AI ranks recommendations and finds answers.

Understand how linked lists, stacks, and queues handle dynamic data in AI systems and beyond.

See how trees and graphs represent hierarchical and interconnected data powering AI decision-making and recommendations.

Master heaps, the priority queue abstraction, and classic patterns like top-K problems and merging sorted lists.

Learn the binary search mindset: search spaces, binary search on answer, and patterns that appear in dozens of interview problems.

Understand recursion from the call stack up, then learn the backtracking template that solves permutations, N-Queens, and beyond.

Learn when making the locally optimal choice at each step actually produces the globally optimal result, and when it doesn't.

Master 2D grid traversal with BFS, DFS, flood fill, multi-source BFS, spiral traversal, and dynamic programming on grids.