Algorithms: From Zero to Hero (A Beginner's Guide)
Algorithms: From Zero to Hero (A Beginner's Guide)
Welcome to the definitive beginner's guide to algorithms! This comprehensive 60-lesson course is designed to take you from knowing nothing about computational thinking and efficiency to mastering fundamental data structures, sorting techniques, advanced graph algorithms, and critical algorithmic paradigms like Dynamic Programming and Greedy approaches. You will learn the 'why' behind efficient code, mastering the crucial skill of algorithmic analysis using Big O notation. By the end of this journey, you will possess a robust foundation for tackling technical interviews and building highly optimized software solutions.
Lessons
- Lesson 1: What is an Algorithm?
- Lesson 2: Why Study Algorithms?
- Lesson 3: Algorithms in Daily Computing
- Lesson 4: Measuring Performance: Time and Space
- Lesson 5: Introduction to Big O Notation
- Lesson 6: Analyzing Constant Time: O(1)
- Lesson 7: Analyzing Linear Time: O(N)
- Lesson 8: Analyzing Quadratic Time: O(N²)
- Lesson 9: Analyzing Logarithmic Time: O(log N)
- Lesson 10: Worst, Average, and Best Case Scenarios
- Lesson 11: Arrays and Linked Lists (Comparison)
- Lesson 12: Stacks (LIFO) and Queues (FIFO)
- Lesson 13: Hash Tables (Mapping Keys to Values)
- Lesson 14: Introduction to Recursion (The Concept)
- Lesson 15: Recursion vs. Iteration
- Lesson 16: Bubble Sort: Concept and Implementation
- Lesson 17: Analyzing Bubble Sort Efficiency
- Lesson 18: Selection Sort: Concept and Implementation
- Lesson 19: Insertion Sort: Concept and Implementation
- Lesson 20: Practical Comparison of O(N²) Sorts
- Lesson 21: Merge Sort: The Divide and Conquer Approach (Part 1)
- Lesson 22: Merge Sort: Implementation and Merging Step (Part 2)
- Lesson 23: Quick Sort: The Pivot Strategy (Part 1)
- Lesson 24: Quick Sort: Partitioning and Recursion (Part 2)
- Lesson 25: Comparing N log N Sorts (Merge vs. Quick)
- Lesson 26: Linear Search: Simple and Effective
- Lesson 27: Binary Search: Requirement and Implementation (O(log N))
- Lesson 28: Advanced Binary Search Applications
- Lesson 29: Hashing and Collision Resolution
- Lesson 30: Bloom Filters (Introduction to Probabilistic Structures)
- Lesson 31: Introduction to Trees (Terminology, Root, Nodes)
- Lesson 32: Binary Search Trees (BSTs): Insertion and Search
- Lesson 33: Tree Traversal Algorithms: Inorder, Preorder, Postorder
- Lesson 34: Heaps: Max Heap and Min Heap structure
- Lesson 35: Heap Sort Algorithm
- Lesson 36: Introduction to Graphs (Nodes, Edges, Types)
- Lesson 37: Representing Graphs: Adjacency Matrix vs. Adjacency List
- Lesson 38: Depth First Search (DFS): Concept and Implementation
- Lesson 39: Breadth First Search (BFS): Concept and Implementation
- Lesson 40: DFS vs. BFS: Choosing the Right Traversal
- Lesson 41: Weighted Graphs and Shortest Path Problem
- Lesson 42: Dijkstra's Algorithm: Introduction and Greediness
- Lesson 43: Dijkstra's Algorithm: Detailed Implementation Steps
- Lesson 44: Handling Negative Weights: Introduction to Bellman-Ford
- Lesson 45: All-Pairs Shortest Path: Introduction to Floyd-Warshall
- Lesson 46: Introduction to Algorithmic Paradigms
- Lesson 47: Divide and Conquer: Reviewing Merge Sort/Quick Sort
- Lesson 48: Greedy Algorithms: Characteristics and Pitfalls
- Lesson 49: Example of Greedy: Coin Change Problem (Simple Case)
- Lesson 50: Minimum Spanning Trees (MST): Prim's Algorithm Concept
- Lesson 51: Kruskal's Algorithm: Using the Disjoint Set Union (DSU) structure
- Lesson 52: Introduction to Dynamic Programming (DP)
- Lesson 53: DP Example 1: Memoization (Top-Down Approach)
- Lesson 54: DP Example 2: Tabulation (Bottom-Up Approach - Fibonacci)
- Lesson 55: DP Example 3: Knapsack Problem (0/1 Introduction)
- Lesson 56: String Matching Algorithms: Introduction to Naive Approach
- Lesson 57: Introduction to Complexity Classes: P vs. NP
- Lesson 58: Backtracking Algorithms: N-Queens Problem Concept
- Lesson 59: Probabilistic Algorithms and Randomized Quick Sort
- Lesson 60: Course Conclusion, Review, and Next Steps