Modern Graph Theory Algorithms with Python (2025)

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[ WebToolTip.com ] Modern Graph Theory Algorithms with Python (2025)
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction to Graph Theory and Python for Graphs
    • 1 -What is Graph Theory (Brief Overview).mp4 (52.9 MB)
    • 2 -Types of Graphs (Directed, Undirected, Weighted).mp4 (57.4 MB)
    • 3 -Introduction to Python for Graphs.mp4 (48.9 MB)
    • 4 -Working with NetworkX for Graph Creation.mp4 (45.5 MB)
    10 - End-of-course Projects
    • 1 -Graph-Based Recommendation System.mp4 (53.1 MB)
    • 2 -Advanced Network Flow Optimization.mp4 (51.3 MB)
    • 3 -Social Network Analysis Project.mp4 (51.8 MB)
    2 - Social Network Representation (project1)
    • 1 -Creating a Simple Social Network Graph.mp4 (23.6 MB)
    • 2 -Adding Nodes and Edges.mp4 (32.4 MB)
    • 3 -Visualizing the Graph using Matplotlib.mp4 (20.2 MB)
    • 4 -Analysis of Basic Graph Properties (Degree, Path Length).mp4 (29.9 MB)
    3 - Graph Traversal Algorithms
    • 1 -Depth-First Search (DFS).mp4 (29.0 MB)
    • 2 -Breadth-First Search (BFS).mp4 (26.8 MB)
    • 3 -Recursive vs Iterative Implementations.mp4 (44.1 MB)
    • 4 -Application Graph Exploration.mp4 (33.2 MB)
    4 - Shortest Path in a City Map (project 2)
    • 1 -Representing a City Map as a Graph.mp4 (25.9 MB)
    • 2 -Implementing Dijkstra’s Algorithm to Find Shortest Paths.mp4 (16.4 MB)
    • 3 -Visualizing the Path with Weights.mp4 (33.3 MB)
    • 4 -Analyzing the Performance of the Algorithm.mp4 (44.5 MB)
    5 - Graph Search and Connectivity
    • 1 -Connected Components.mp4 (33.0 MB)
    • 2 -Articulation Points and Bridges.mp4 (26.7 MB)
    • 3 -Bipartite Graphs.mp4 (24.9 MB)
    • 4 -Real-World Application Network Resilience.mp4 (32.0 MB)
    6 - Minimum Spanning Tree (MST) Algorithms
    • 1 -Kruskal’s Algorithm.mp4 (30.0 MB)
    • 2 -Prim’s Algorithm.mp4 (26.7 MB)
    • 3 -Applications of MST in Network Design.mp4 (31.5 MB)
    • 4 -Implementing MST Algorithms in Python.mp4 (41.0 MB)
    7 - Designing an Optimal Network (project3)
    • 1 -Creating a Network for Fiber Optic Cable Installation.mp4 (14.9 MB)
    • 2 -Applying MST Algorithms (Prim’s and Kruskal’s).mp4 (18.9 MB)
    • 3 -Visualizing the Optimal Network Design.mp4 (22.0 MB)
    • 4 -Cost Analysis and Efficiency.mp4 (30.4 MB)
    8 - Graph Algorithms for Social Networks
    • 1 -Centrality Measures (Degree, Betweenness, Closeness).mp4 (26.7 MB)
    • 2 -Community Detection Algorithms.mp4 (30.6 MB)
    • 3 -PageRank Algorithm.mp4 (26.9 MB)
    • 4 -Graph-Based Applications in Social Media.mp4 (32.2 MB)
    9 - Graph Algorithms in Real-World Applications
    • 1 -Graph-Based Machine Learning.mp4 (26.4 MB)
    • 2 -Graphs in Biology.mp4 (25.3 MB)
    • 3 -Graphs in Transportation and Networks.mp4 (28.4 MB)
    • 4 -Graphs in Search Engines.mp4 (29.6 MB)
    • Bonus Resources.txt (0.1 KB)

Description

Modern Graph Theory Algorithms with Python (2025)

https://WebToolTip.com

Published 2/2025
Created by Meta Brains,Skool of AI
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 39 Lectures ( 2h 21m ) | Size: 1.24 GB

Master NetworkX, Social Network Analysis & Shortest Path Algorithms - Build 4 Professional Projects with Graph Theory

What you'll learn
Master fundamental graph theory algorithms including DFS, BFS, Dijkstra's Algorithm, and implement them efficiently using Python and NetworkX
Build a complete social network analyzer from scratch, including visualization tools and community detection algorithms
Implement and optimize pathfinding algorithms for real-world applications like city navigation systems and transportation networks
Design and develop optimal network infrastructure using Minimum Spanning Tree algorithms (Kruskal's and Prim's)
Create professional graph visualizations using NetworkX and Matplotlib, including interactive network displays and analysis tools
Apply centrality measures and PageRank algorithms to analyze influence and importance in social networks
Develop a recommendation system using graph-based algorithms and machine learning techniques
Master advanced network analysis techniques including community detection, bipartite graphs, and articulation points
Build four complete real-world projects that nstrate practical applications of graph theory in modern software development

Requirements
Basic Python programming experience (variables, functions, loops, and basic data structures). No advanced Python knowledge required
Basic understanding of data structures (arrays, lists, dictionaries). No prior graph theory knowledge needed
Python 3.x installed on your computer (Windows, Mac, or Linux)
Familiarity with using pip to install Python packages (we'll guide you through installing NetworkX and Matplotlib)
Basic math skills (high school level algebra). No advanced mathematics required
A computer with minimum 4GB RAM and any modern operating system
Text editor or IDE of your choice (we recommend VS Code, but any will work)
Enthusiasm to learn about networks and graph algorithms - perfect for beginners in graph theory!



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Modern Graph Theory Algorithms with Python (2025)

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1.2 GB
seeders:19
leechers:4
Modern Graph Theory Algorithms with Python (2025)

Torrent hash: 7586AAA8890F1F937AA66DA9EFB03F5BE1780AC5