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The course opens with complexity analysis and the classic building blocks, then extends into a long run of design techniques, from divide and conquer to branch and bound.
C# developers who want the theory-heavy route through algorithm design, not only the built-in collections. Expect a long course that rewards steady, section-by-section study. Because the advanced sections build on the earlier analysis material, starting at the top and skipping ahead tends to cost more time than it saves.
This course will help you in better understanding of the basics of Data Structures and how algorithms are implemented in C#. This course consists of Videos which covers the theory concepts + implementation in C#.
There’s tons of concepts and content in this course:
Basics of data structures & Algorithms
Analysis of Algorithms (Big O, Time and Space complexity)
Recursion & Analysis of Recursive Algorithms
Searching Algorithms
Sorting Algorithms
Linked List
Stacks
Queues
Binary Trees
Binary Search Trees
Balanced Binary Search Trees
Priority Queues and Heaps
Hashing
Graphs
Graph Traversal Algorithms
Followed by Advanced Topics of Algorithms:
Sets and Disjoint Sets
Divide and Conquer Approach - Introduction
Divide and Conquer - Binary Search
Divide and Conquer - Finding Maximum and Mininum
Divide and Conquer - Merge Sort
Divide and Conquer - Quick Sort
Divide and Conquer - Selection Algorithm
Divide and Conquer - Strassens Matrix Multiplication
Divide and Conquer - Closest Pair
Divide and Conquer - Convex Hull
Greedy Method - Introduction
Greedy Method - Knapsack Problem
Greedy Method - Job Sequencing with Deadlines
Greedy Method - Mininum Cost Spanning Tree (Prim's & Kruskal's Algorithms)
Greedy Method - Optimal Storage on Trees
Greedy Method - Optimal Merge Pattern
Greedy Method - Single Source Shortest Path (Dijkstra's Algorithm)
Dynamic Programming - Introduction
Dynamic Programming - Multistage Graphs
Dynamic Programming - All Pairs Shortest Path
Dynamic Programming - Single Source Shortest Path
Dynamic Programming - Optimal Binary Search Trees
Dynamic Programming - 0/1 Knapsack Problem
Dynamic Programming - Reliability Design
Dynamic Programming - Travelling Salespersons Problem
Backtracking - Introduction
Backtracking - n-Queesn Problem
Backtracking - Sum of Subsets Problem
Backtracking - Graph Coloring Problem
Backtracking - Hamiltonian Cycles Problem
Backtracking - 0/1 Knapsack Problem
Branch & Bound - Introduction
Branch & Bound - n-Queens Problem
Branch & Bound - Job Sequencing Problem
Branch & Bound - 0/1 Knapsack Problem
Again, each of these sections includes detailed videos tutorial.
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