Graph Theory And It'S Algorithms

What you'll learn
Understand the Graph Data Structure and Know how to implement it
Understand the algorithms of Graph Theory
Know the concepts of Graph Theory
Learn the Python implementation of Graph Algorithms
Requirements
No. But a knowledge in Basic Data Structures is preferred.
Description
I welcome you all to my course on 'Graph Theory and it's Algorithms - Advanced DSA'This course deals with the concepts of Graph Theory such as1. What is Graph Data Structure? 2. Applications of Graphs to solve real life problems. 3. Terminologies involved in Graph Theory4. Types of Graph Data Structure - Weighted, Unweighted, Directed, Undirected, Cyclic, Acyclic, Directed Acyclic Graphs. This course also gives the explanation of the following algorithms and also provide their implementation in Python. 1. Representation of Graphs - Adjacency List, Adjacency Matrix. 2. Implementation of Adjacency List, Adjacency Matrix using OOPS in Python. 3. Depth First Search (DFS) Algorithm in Python4. Breadth First Search (BFS) 5. Problems based on DFS - Topological Sort, Sum, Max, Min. Single Source Shortest Path Problems. 1. Djikstra's Algorithm - Algorithm and Code in Python. 2. Bellman Ford - Algorithm and Code in Python.Minimum Spanning Tree Problems1. Explanation of Spanning Trees, Finding out Minimum Spanning Tree. 2. Prim's and Kruskal's Algorithm. Note: Knowledge in Basic Data Structures and Python is preferred. A graph data structure consists of a finite (and possibly mutable) set of vertices (also called nodes or points), together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are known as edges (also called links or lines), and for a directed graph are also known as edges but also sometimes arrows or arcs. The vertices may be part of the graph structure, or may be external entities represented by integer indices or references.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 What is a graph, Applications of graphs
Lecture 3 Graph Terminologies
Lecture 4 Weighted and unweighted graphs
Lecture 5 Cyclic and Acyclic Graphs
Lecture 6 Directed, Undirected, DAG Graphs
Section 2: Prerequisites - Recursion
Lecture 7 Understanding Recursion with an example
Lecture 8 Example 2 for Recursion - Tracing the output
Lecture 9 Tricks to write recursive functions !
Lecture 10 How to convert an iterative solution into a recursive solution
Section 3: Implementation of Graphs
Lecture 11 Adjacency Lists Representations of a Graph
Lecture 12 Adjacency Matrix Representation of a Graph
Lecture 13 Implementation of a Graph
Section 4: Graph Traversal Algorithms
Lecture 14 Depth First Search (DFS) - Algorithm
Lecture 15 Code for DFS
Lecture 16 Tracing the code for DFS
Lecture 17 Breadth First Search (BFS) Algorithm
Lecture 18 Code for BFS
Section 5: Problems based on Depth First Search (DFS)
Lecture 19 The concept of Topological Sort
Lecture 20 Implementation of TSS
Lecture 21 Code for Topological Sort
Lecture 22 Sum of all the nodes in a graph
Lecture 23 Maximum of all the nodes in the graph
Lecture 24 Minimum of all the nodes in a graph
Section 6: Single Source Shortest Path Algorithms
Lecture 25 Single Source Shortest Path Problems - Intro
Lecture 26 Djikstra's Algorithm - I
Lecture 27 Djikstra's Algorithm - II
Lecture 28 Example 1 - Djikstra's Algorithm
Lecture 29 Code for Djikstra's Algorithm'
Lecture 30 Bellmann Ford Algorithm
Lecture 31 Dry run of Bellman Ford Algorithm
Lecture 32 Code for Bellman Ford Algorithm
Section 7: Minimum Spanning Trees
Lecture 33 Minimum Spanning Tree
Lecture 34 Prim's Algorithm
Beginner Programmers,Beginner DSA Learners

Homepage

https://www.udemy.com/course/graph-theory-and-its-algorithms/