Learn Algorithms - Expanded Project Guides

Generated from: LEARN_ALGORITHMS_DEEP_DIVE.md

This folder contains deep-dive guides for each project in the learning path.

Overview

These projects move from foundational algorithm analysis to advanced paradigms (graph algorithms, dynamic programming, greedy methods, and complexity theory). Each project expands the original guide into a full specification, architecture, implementation plan, tests, and learning resources.

Project Index

# Project Difficulty Time Key Focus
1 Algorithm Complexity Visualizer Level 1: Beginner Weekend Complexity analysis, Big O
2 Sorting Algorithm Showdown Level 2: Intermediate 1-2 weeks Sorting, divide and conquer
3 Binary Search Everything Level 1: Beginner Weekend Searching, binary search variants
4 Recursion Visualizer and Practice Level 2: Intermediate 1-2 weeks Recursion, call stack
5 Linked List From Scratch Level 2: Intermediate 1-2 weeks Pointers, memory, lists
6 Stack and Queue Applications Level 2: Intermediate 1-2 weeks Stacks, queues
7 Hash Table From Scratch Level 3: Advanced 2 weeks Hashing, collisions, amortized analysis
8 Binary Tree and BST Complete Implementation Level 3: Advanced 2 weeks Trees, traversals
9 Heap and Priority Queue Level 2: Intermediate 1-2 weeks Heaps, priority queues
10 Graph Algorithms Suite Level 3: Advanced 3-4 weeks Graph traversal, shortest paths
11 Backtracking Puzzle Solver Level 3: Advanced 2-3 weeks Backtracking, constraints
12 Dynamic Programming Mastery Level 4: Expert 3-4 weeks DP, optimization
13 Greedy Algorithms Collection Level 3: Advanced 2 weeks Greedy, proof techniques
14 String Algorithms Toolkit Level 4: Expert 3 weeks Pattern matching, tries
15 Computational Complexity Explorer Level 5: Master 1 month NP-completeness, reductions

Learning Paths

  • New to algorithms: 1, 3, 4, 2
  • Data structure foundations: 5, 6, 7, 8, 9
  • Interview focus: 2, 3, 7, 8, 10, 11, 12
  • Full depth: 1 through 15 in order

Prerequisites

  • Comfort with a general-purpose language (Python or C)
  • Basic data structures (arrays, strings, functions)
  • Willingness to read and reason about time complexity