Graph Algorithms for Data Science, Video Edition

Graph Algorithms for Data Science, Video Edition

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 9h 45m | 1.51 GB

Practical methods for analyzing your data with graphs, revealing hidden connections and new insights.

Graphs are the natural way to represent and understand connected data. This video course explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

In Graph Algorithms for Data Science, Video Edition you will learn:

• Labeled-property graph modeling
• Constructing a graph from structured data such as CSV or SQL
• NLP techniques to construct a graph from unstructured data
• Cypher query language syntax to manipulate data and extract insights
• Social network analysis algorithms like PageRank and community detection
• How to translate graph structure to a ML model input with node embedding models
• Using graph features in node classification and link prediction workflows

Graph Algorithms for Data Science, Video Edition is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.

A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This video tutorial presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more.

Graph Algorithms for Data Science, Video Edition shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge tutorial also demonstrates how you can create graphs that optimize input for AI models using node embedding.