Published 2/2025
Created by Meta Brains,Skool of AI
MP4 | Video: h264, 1280×720 | 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 demonstrate 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!
Description
Dive into the fascinating world of Graph Theory and its practical applications with this comprehensive, project-based course. Whether you’re a data scientist, software engineer, or algorithm enthusiast, you’ll learn how to solve real-world problems using graph algorithms in Python.This course stands out by combining theoretical foundations with hands-on implementation, featuring four carefully designed projects that progressively build your expertise. You’ll start with the basics of graph theory and quickly advance to implementing sophisticated algorithms using NetworkX, Python’s powerful graph library.Key features of this course include:Building a social network analyzer from scratchImplementing pathfinding algorithms for city navigation systemsDesigning optimal network infrastructure using MST algorithmsCreating a professional recommendation systemYou’ll master essential algorithms including Depth-First Search, Breadth-First Search, Dijkstra’s Algorithm, and advanced concepts like PageRank and community detection. Each topic is reinforced through practical exercises and real-world applications, from social media analysis to transportation network optimization.The course includes complete Python implementations of all algorithms, with a focus on both efficiency and readability. You’ll learn industry best practices for working with NetworkX and visualization tools like Matplotlib, making your graph analysis both powerful and visually compelling.Perfect for intermediate Python programmers who want to expand their algorithmic toolkit, this course requires basic Python knowledge but assumes no prior experience with graph theory or NetworkX. By the end, you’ll be able to analyze complex networks, optimize transportation systems, and build graph-based machine learning solutions.Join us to transform your understanding of graph algorithms from theoretical concepts into practical, employable skills through hands-on projects and real-world applications.
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