Graph Theory And It'S Algorithms

Learn the concepts of Graph Theory, it's Algorithms and Implement them in Python

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