DSA - Abdul Bari
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7 learners
What you'll learn
This course includes
- 48 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 93 lessons • 48 hours of video
DSA - Abdul Bari
93 lessons
• 27.3 hours
DSA - Abdul Bari
93 lessons
• 27.3 hours
- 1. Introduction to Algorithms 11:49
- 1.1 Priori Analysis and Posteriori Testing 01:48
- 1.2 Characteristics of Algorithm 05:37
- 1.3 How Write and Analyze Algorithm 10:37
- 1.4 Frequency Count Method 12:22
- 1.5.1 Time Complexity #1 09:44
- 1.5.2 Time Complexity Example #2 14:13
- 1.5.3 Time Complexity of While and if #3 21:54
- 1.6 Classes of functions 03:10
- 1.7 Compare Class of Functions 05:11
- 1.8.1 Asymptotic Notations Big Oh - Omega - Theta #1 15:46
- 1.8.2 Asymptotic Notations - Big Oh - Omega - Theta #2 10:07
- 1.9 Properties of Asymptotic Notations 11:58
- 1.10.1 Comparison of Functions #1 09:28
- 1.10.2 Comparison of Functions #2 10:26
- 1.11 Best Worst and Average Case Analysis 18:56
- 1.12 Disjoint Sets Data Structure - Weighted Union and Collapsing Find 26:04
- 2 Divide And Conquer 07:04
- 2.1.1 Recurrence Relation (T(n)= T(n-1) + 1) #1 13:43
- 2.1.2 Recurrence Relation (T(n)= T(n-1) + n) #2 16:00
- 2.1.3 Recurrence Relation (T(n)= T(n-1) + log n) #3 12:25
- 2.1.4 Recurrence Relation T(n)=2 T(n-1)+1 #4 10:42
- 2.2 Masters Theorem Decreasing Function 08:10
- 2.3.1 Recurrence Relation Dividing Function T(n)=T(n/2)+1 #1 08:41
- 2.3.2 Recurrence Relation Dividing [ T(n)=T(n/2)+ n]. #2 07:26
- 2.3.3 Recurrence Relation [ T(n)= 2T(n/2) +n] #3 11:20
- 2.4.1 Masters Theorem in Algorithms for Dividing Function #1 16:50
- 2.4.2 Examples for Master Theorem #2 05:41
- 2.5 Root function (Recurrence Relation) 05:37
- 2.6.1 Binary Search Iterative Method 19:36
- 2.6.2 Binary Search Recursive Method 07:11
- 2.6.3 Heap - Heap Sort - Heapify - Priority Queues 51:08
- 2.7.1 Two Way MergeSort - Iterative method 20:19
- 2.7.2. Merge Sort Algorithm 20:23
- 2.7.3 MergeSort in-depth Analysis 13:28
- 2.8.1 QuickSort Algorithm 13:43
- 2.8.2 QuickSort Analysis 11:37
- 2.9 Strassens Matrix Multiplication 23:40
- 3. Greedy Method - Introduction 12:02
- 3.1 Knapsack Problem - Greedy Method 15:30
- 3.2 Job Sequencing with Deadlines - Greedy Method 13:29
- 3.3 Optimal Merge Pattern - Greedy Method 09:33
- 3.4 Huffman Coding - Greedy Method 17:33
- 3.5 Prims and Kruskals Algorithms - Greedy Method 20:12
- 3.6 Dijkstra Algorithm - Single Source Shortest Path - Greedy Method 18:35
- 4 Principle of Optimality - Dynamic Programming introduction 14:52
- 4.1 MultiStage Graph - Dynamic Programming 21:07
- 4.1.1 MultiStage Graph (Program) - Dynamic Programming 14:26
- 4.2 All Pairs Shortest Path (Floyd-Warshall) - Dynamic Programming 14:13
- 4.3 Matrix Chain Multiplication - Dynamic Programming 23:00
- [New] Matrix Chain Multiplication using Dynamic Programming Formula 52:02
- 4.3.1 Matrix Chain Multiplication (Program) - Dynamic Programming 18:40
- 4.4 Bellman Ford Algorithm - Single Source Shortest Path - Dynamic Programming 17:12
- 4.5 0/1 Knapsack - Two Methods - Dynamic Programming 28:24
- 4.5.1 0/1 Knapsack Problem (Program) - Dynamic Programming 17:00
- 4.6 Optimal Binary Search Tree (Successful Search Only) - Dynamic Programming 30:19
- 4.6.2 [New] Optimal Binary Search Tree Successful and Unsuccessful Probability - Dynamic Programming 57:00
- 4.7 [New] Traveling Salesman Problem - Dynamic Programming using Formula 17:18
- 4.8 Reliability Design - Dynamic Programming 26:32
- 4.9 Longest Common Subsequence (LCS) - Recursion and Dynamic Programming 23:35
- 5.1 Graph Traversals - BFS & DFS -Breadth First Search and Depth First Search 18:31
- 5.2 Articulation Point and Biconnected Components 08:37
- 6 Introduction to Backtracking - Brute Force Approach 08:15
- 6.1 N Queens Problem using Backtracking 13:41
- 6.2 Sum Of Subsets Problem - Backtracking 12:19
- 6.3 Graph Coloring Problem - Backtracking 15:52
- 6.4 Hamiltonian Cycle - Backtracking 18:35
- 7 Branch and Bound Introduction 09:40
- 7.1 Job Sequencing with Deadline - Branch and Bound 10:56
- 7.2 0/1 Knapsack using Branch and Bound 10:48
- 7.3 Traveling Salesman Problem - Branch and Bound 24:42
- 8. NP-Hard and NP-Complete Problems 31:53
- 8.1 NP-Hard Graph Problem - Clique Decision Problem 17:14
- 9.1 Knuth-Morris-Pratt KMP String Matching Algorithm 18:56
- 9.2 Rabin-Karp String Matching Algorithm 23:50
- 10.1 AVL Tree - Insertion and Rotations 43:08
- 10.2 B Trees and B+ Trees. How they are useful in Databases 39:41
- Asymptotic Notations - Simplified 22:44
- Hashing Technique - Simplified 17:04
- Shortest Path Algorithms (Dijkstra and Bellman-Ford) - Simplified 26:13
- BFS DFS - Simplified 19:13
- Tower of Hanoi Problem - Made Easy 09:32
- Row-Major and Column-Major Mapping 19:16
- Merge Sort Algorithm - Hindi 16:38
- Bresenham's Line Drawing Algorithm 43:39
- DDA Line Drawing Algorithm - Computer Graphics 26:48
- 6. Everything about Variables in JAVA 29:05
- 5. Everything about JAVA Data Types 27:54
- 4. Why public static void main(String args[ ]) ? JAVA 13:18
- 3. Why to Set Path ? JAVA 14:02
- 2. Why and How Java is platform independent 19:04
- 1. Why One should Learn Java - Introduction to Java 14:27
- 4.7 Traveling Salesperson Problem - Dynamic Programming 15:25
