| 1 -Centrality Measures (Degree, Betweenness, Closeness).mp4 | 26.7 MB | ||
| 1 -Connected Components.mp4 | 33 MB | ||
| 1 -Creating a Network for Fiber Optic Cable Installation.mp4 | 14.9 MB | ||
| 1 -Creating a Simple Social Network Graph.mp4 | 23.6 MB | ||
| 1 -Depth-First Search (DFS).mp4 | 29 MB | ||
| 1 -Graph-Based Machine Learning.mp4 | 26.4 MB | ||
| 1 -Graph-Based Recommendation System.mp4 | 53.1 MB | ||
| 1 -Kruskal’s Algorithm.mp4 | 30 MB | ||
| 1 -Representing a City Map as a Graph.mp4 | 25.9 MB | ||
| 1 -What is Graph Theory (Brief Overview).mp4 | 52.9 MB | ||
| 2 -Adding Nodes and Edges.mp4 | 32.4 MB | ||
| 2 -Advanced Network Flow Optimization.mp4 | 51.3 MB | ||
| 2 -Applying MST Algorithms (Prim’s and Kruskal’s).mp4 | 18.9 MB | ||
| 2 -Articulation Points and Bridges.mp4 | 26.7 MB | ||
| 2 -Breadth-First Search (BFS).mp4 | 26.8 MB | ||
| 2 -Community Detection Algorithms.mp4 | 30.6 MB | ||
| 2 -Graphs in Biology.mp4 | 25.3 MB | ||
| 2 -Implementing Dijkstra’s Algorithm to Find Shortest Paths.mp4 | 16.4 MB | ||
| 2 -Prim’s Algorithm.mp4 | 26.7 MB | ||
| 2 -Types of Graphs (Directed, Undirected, Weighted).mp4 | 57.4 MB | ||
| 3 -Applications of MST in Network Design.mp4 | 31.5 MB | ||
| 3 -Bipartite Graphs.mp4 | 24.9 MB | ||
| 3 -Graphs in Transportation and Networks.mp4 | 28.4 MB | ||
| 3 -Introduction to Python for Graphs.mp4 | 48.9 MB | ||
| 3 -PageRank Algorithm.mp4 | 26.9 MB | ||
| 3 -Recursive vs Iterative Implementations.mp4 | 44.1 MB | ||
| 3 -Social Network Analysis Project.mp4 | 51.8 MB | ||
| 3 -Visualizing the Graph using Matplotlib.mp4 | 20.2 MB | ||
| 3 -Visualizing the Optimal Network Design.mp4 | 22 MB | ||
| 3 -Visualizing the Path with Weights.mp4 | 33.3 MB | ||
| 4 -Analysis of Basic Graph Properties (Degree, Path Length).mp4 | 29.9 MB | ||
| 4 -Analyzing the Performance of the Algorithm.mp4 | 44.5 MB | ||
| 4 -Application Graph Exploration.mp4 | 33.2 MB | ||
| 4 -Cost Analysis and Efficiency.mp4 | 30.4 MB | ||
| 4 -Graph-Based Applications in Social Media.mp4 | 32.2 MB | ||
| 4 -Graphs in Search Engines.mp4 | 29.6 MB | ||
| 4 -Implementing MST Algorithms in Python.mp4 | 41 MB | ||
| 4 -Real-World Application Network Resilience.mp4 | 32 MB | ||
| 4 -Working with NetworkX for Graph Creation.mp4 | 45.5 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 41 total files | |||
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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