Amazing Graph Algorithms : Coding in Java,JavaScript, Python

Amazing Graph Algorithms : Coding in Java,JavaScript, Python, available at $69.99, has an average rating of 4.5, with 68 lectures, based on 45 reviews, and has 1001 subscribers.

You will learn about Graph Algorithms Programming Algorithms This course is ideal for individuals who are Who wants to deep dive into graphs or Want to solve some super complicated graph Algorithms It is particularly useful for Who wants to deep dive into graphs or Want to solve some super complicated graph Algorithms.

Enroll now: Amazing Graph Algorithms : Coding in Java,JavaScript, Python

Summary

Title: Amazing Graph Algorithms : Coding in Java,JavaScript, Python

Price: $69.99

Average Rating: 4.5

Number of Lectures: 68

Number of Published Lectures: 68

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 68

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Graph Algorithms
Programming Algorithms

Who Should Attend

Who wants to deep dive into graphs
Want to solve some super complicated graph Algorithms

Target Audiences

Who wants to deep dive into graphs
Want to solve some super complicated graph Algorithms

Graphs are Amazing!

We will have a lot to cover in this course also the course is coded in Java, JavaScript & Python.

While solving graph algorithms, We may need to visit and process each node present in the graph. And for that, we must know how to traverse the graphs efficiently,

So, first, we will cover graph traversal, where we gonna see the 2 types of graph traversals, Depth First Search, and Breadth-first Search.

Then we will understand Spanning Trees and will see famous algorithms to find minimum cost spanning tree, basically, a minimum cost spanning tree is a tree from the graph connecting all the vertices with single edges each and that all

Of the lowest cost, so to minimize the cost to connect all the vertices.

For example :

Suppose, you own a telecommunication company

and you have towers that spread across the state.

You want to connect them so that data can be passed from one tower to others.

Connecting different towers involve different costs, so the problem is how will you minimize the cost. Here, comes the need of using Minimum spanning tree algorithms to find

That tree connecting all the towers with edges that have a minimum cost, so that the spanning Tree cost is minimum.

After that, we will look to Shortest Path algorithms, these are useful to find the shortest distance from of a source from all the other vertices (called single-source shortest path)

or shortest distance of each vertex with all the

Other vertices, that’s called finding all pair shortest path.

For example, finding the distance of a city, let’s say Istambul to all the other famous cities of turkey.

Or let’s say A person who is planning a trip may need to answer questions such as, “What is the least expensive way to get from Princeton to San Jose?” A person more interested in time than in money may need to know the answer to the question “What is the fastest way to get from Princeton to San Jose?” To answer such questions, we process information about connections (travel routes) between items (towns and cities).

Then we will move to Flow network problems. These are concerned with the networks or graph, having a flow going through it.

There will be problems that ask to maximize the flow across the network or problems that ask to disconnect the source from the destination or sink in minimum cost.

After that we will discuss, algorithms to find strongly connected components in a graph.

Hope you will enjoy the course.

Happy Learning

Course Curriculum

Chapter 1: Understanding Graphs

Lecture 1: Graphs – In Real World

Lecture 2: Google's Knowledge Graph

Lecture 3: Graphs – Overview

Lecture 4: Terminologies

Lecture 5: Identification of Problem

Lecture 6: Approaching the Problem

Lecture 7: Journey : What We Are Going To Cover

Chapter 2: Let's Get Started

Lecture 1: Course Resources

Chapter 3: Graph Traversal Algorithms

Lecture 1: Graph Traversal

Lecture 2: Depth First Search Traversal – DFS

Lecture 3: DFS – Recursive Java Implementation

Lecture 4: DFS – Iterative Java Implementation

Lecture 5: DFS – Recursive Javascript Implementation

Lecture 6: DFS – Iterative Javascript Implementation

Lecture 7: DFS – Recursive Python Implementation

Lecture 8: DFS – Complexity Analysis

Lecture 9: Breadth First Search Traversal

Lecture 10: BFS – Java Implementation

Lecture 11: BFS – Javascript Implementation

Lecture 12: BFS – Python Implementation

Lecture 13: BFS – Complexity Analysis

Chapter 4: Minimum Spanning Tree Algorithms

Lecture 1: What Are Spanning Trees; What is MST?

Lecture 2: Prim's Algorithm

Lecture 3: Prim's Algorithm – Java Implementation

Lecture 4: Prim's Algorithm – Javascript Implementation

Lecture 5: Prim's Algorithm – Python Implementation

Lecture 6: Kruskal's Algorithm

Lecture 7: Union-Find Algorithm

Lecture 8: Kruskal's Algorithm – java Implementation

Lecture 9: Kruskal's Algorithm – Javascript Implementation

Lecture 10: Kruskal's Algorithm – Python Implementation

Chapter 5: Shortest Path Algorithms

Lecture 1: Finding Shortest Path

Lecture 2: Dijkstra's Algorithm

Lecture 3: Dijkstra's Algorithm – Java Implementation

Lecture 4: Dijkstra's Algorithm – Javascript Implementation

Lecture 5: Dijkstra's Algorithm – Python Implementation

Lecture 6: BellmanFord's Algo

Lecture 7: BellmanFord's Algo Live Code Java

Lecture 8: BellmanFord's Algo Live Code Javascript

Lecture 9: BellmanFord's Algo Live Code Python

Lecture 10: Floyd Warshall Algorithm

Lecture 11: Floyd-Warshall Algorithm – Java Implementation

Lecture 12: Floyd-Warshall Algorithm – Javascript Implementation

Lecture 13: Floyd-Warshall Algorithm – Python Implementation

Lecture 14: Johnson's Algorithm

Lecture 15: Johnson's Algorithm – Java Implementation

Lecture 16: Johnson's Algorithm – Javascript Implementation

Lecture 17: Johnson's Algorithm – Python Implementation

Chapter 6: Network Flow Algorithms

Lecture 1: What Are Flow Networks?

Lecture 2: Ford-Fulkerson Algorithm

Lecture 3: Ford-Fulkerson Algorithm – Edmond's Karp Java Implementation

Lecture 4: Ford-Fulkerson Algorithm – Edmond's Karp Javascript Implementation

Lecture 5: Ford-Fulkerson Algorithm – Edmond's Karp Python Implementation

Lecture 6: Max-Flow Min-Cut Theorem

Chapter 7: Strongly Connected Components

Lecture 1: Strongly Connected Components

Lecture 2: Tarjan's Algorithm

Lecture 3: Tarjan's Algorithm – Java Implementation

Lecture 4: Tarjan's Algorithm – Javascript Implementation

Lecture 5: Tarjan's Algorithm – Python Implementation

Lecture 6: Kosaraju's Algorithm

Lecture 7: Kosaraju's Algorithm – Java Implementation

Lecture 8: Kosaraju's Algorithm – Javascript Implementation

Lecture 9: Kosaraju's Algorithm – Python Implementation

Chapter 8: Others

Lecture 1: Topological Sort : Kahn's Algo

Lecture 2: Topological Sort Live Code Java

Lecture 3: Topological Sort Live Code Javascript

Lecture 4: Topological Sort Live Code Python

Chapter 9: Thank You

Lecture 1: Thank you!

Instructors

Basics Strong
Team of technocrats and Programming lovers

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 17 votes
5 stars: 26 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu