Algorithms Tutorial

1. Algorithms Introduction

What an algorithm is, why the same problem can have wildly different-speed solutions, and a Big-O refresher.

2. Recursion

Base and recursive cases, the call stack visualized, naive vs memoized Fibonacci, and when recursion risks trouble.

3. Sorting Algorithms

Bubble sort, merge sort, and quicksort with full code, a complexity/stability comparison table, and when to just use sorted().

4. Searching Algorithms

Linear vs binary search, why binary search needs sorted data, and solving search in a rotated sorted array.

5. Two Pointers & Sliding Window

Two-pointer two sum and array reversal, sliding window for max subarray sum and longest unique substring, and why both beat O(n²).

6. Divide and Conquer

The divide/conquer/combine pattern, merge sort and binary search revisited through it, and how it differs from dynamic programming.

7. Dynamic Programming

Overlapping subproblems and optimal substructure, top-down vs bottom-up on climbing stairs, and 0/1 knapsack solved with a table walkthrough.

8. Greedy Algorithms

What makes a choice greedy, a coin-change counterexample where greedy fails, and activity selection solved correctly with greedy.

9. Graph Algorithms

Dijkstra's algorithm for weighted shortest paths, when to reach for Bellman-Ford instead, and a complete Union-Find implementation.

10. Big-O Analysis in Practice

How to read nested loops and recursive calls, amortized analysis of dynamic array append, and worked Big-O walkthroughs.

11. Algorithms Interview Questions

Commonly asked algorithms interview questions with clear, practical answers.