Practical guides to the algorithm patterns that show up in real interviews.
Learn the 1D dynamic programming pattern through climbing stairs, house robber, and coin change, with clear Python examples and interview-ready intuition.
Learn the sliding window technique for subarray and substring problems, with Python examples, complexity analysis, and interview-focused walkthroughs.
Learn topological sort for dependency problems like course schedule and task ordering with Python examples, DAG intuition, and interview-ready patterns.
Learn the two pointers pattern for sorted arrays, pair sum, and LeetCode-style interview problems with clear Python examples and walkthroughs.
Learn when to use sliding windows, how fixed and variable windows differ, and how to solve classic substring and subarray problems efficiently.
Understand classic binary search, boundary search, and the broader decision-based pattern that helps solve optimization problems in logarithmic time.
Learn how prefix sums turn repeated range calculations from linear work into constant-time queries, and see how the pattern extends to hash maps and subarray problems.
See how monotonic stacks help solve next greater, previous smaller, and histogram-style problems in linear time without brute force.
Learn the recursive choose-explore-unchoose pattern behind subsets, permutations, combinations, and many classic search problems.
A practical guide to recognizing dynamic programming problems, choosing states, and moving from brute force recursion to memoization and tabulation.
Turn any LeetCode problem into an interactive lesson — trace variables, visualize the algorithm, and practice until it clicks.
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