Pluralsight | Network Analysis in Python: Getting Started [FCO]
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Files
| 0101.Course Overview.mp4 | 8.7 MB | ||
| 0201.Module Outline.mp4 | 5.9 MB | ||
| 0202.Course Prerequisites.mp4 | 6.7 MB | ||
| 0203.Network Science vs. Graph Theory.mp4 | 9.2 MB | ||
| 0204.What Is NetworkX.mp4 | 4.4 MB | ||
| 0205.Manipulating Networks in NetworkX.mp4 | 5.2 MB | ||
| 0206.Graph Theory through NetworkX.mp4 | 7.3 MB | ||
| 0207.Accessing and Modifying Attributes.mp4 | 2.8 MB | ||
| 0208.Graph Storage Formats.mp4 | 9.2 MB | ||
| 0301.Module Outline.mp4 | 1.6 MB | ||
| 0302.Why Network Visualization.mp4 | 4.3 MB | ||
| 0303.Native Visualization in NetworkX.mp4 | 7.4 MB | ||
| 0304.Introduction to Bokeh.mp4 | 3.8 MB | ||
| 0305.Bokeh Plots and Tools.mp4 | 5.9 MB | ||
| 0306.Bokeh Visualizing Node Attributes.mp4 | 6.9 MB | ||
| 0307.A Primer on Visual Network Analysis.mp4 | 19.2 MB | ||
| 0401.Module Outline.mp4 | 2.2 MB | ||
| 0402.Why Centrality Measures.mp4 | 4.5 MB | ||
| 0403.Degree Centrality.mp4 | 2.9 MB | ||
| 0404.Closeness Centrality.mp4 | 6.3 MB | ||
| 0405.Betweenness Centrality.mp4 | 7.8 MB | ||
| 0406.Katz, Eigenvector, and PageRank Centralities.mp4 | 9 MB | ||
| 0407.Community Detection Girvan-Newman Algorithm.mp4 | 6.6 MB | ||
| 0408.Demo Detecting Communities in NetworkX.mp4 | 5.8 MB | ||
| 0501.Module Outline.mp4 | 1.7 MB | ||
| 0502.Motivating Embeddings.mp4 | 8.8 MB | ||
| 0503.Word2vec.mp4 | 22.3 MB | ||
| 0504.Node2vec.mp4 | 30.9 MB | ||
| 0505.Demo Node2vec.mp4 | 17.3 MB | ||
| Discuss.FTUForum.com.html | 31.9 KB | ||
| FTUForum.com.html | 100.4 KB | ||
| FreeCoursesOnline.Me.html | 108.3 KB | ||
| How you can help Team-FTU.txt | 204.8 B | ||
| NulledPremium.com.url | 204.8 B | ||
| Torrent Downloaded From GloDls.to.txt | 102.4 B | ||
| python-network-analysis-getting-started.zip | 12.7 MB | ||
| ▲ 36 total files | |||
Description
Created by : Artur Krochin
Language : English
Updated : Apr 10, 2019
Torrent Contains : 36 Files, 6 Folders
Course Source : https://www.pluralsight.com/courses/python-network-analysis-getting-started
About
Network science is an underutilized part of data science. This course will empower you to leverage the network data your company has. You'll learn about network wrangling and visualization, centralities, communities, and machine learning techniques.
Description
Companies have amassed terabytes of data that can be represented as networks. However, due to a lack of data professionals skilled in network methods, this data is being underutilized. The aim of this course is to fix that and empower you to be able to reason about and build products based on networks. In this course, Network Analysis in Python: Getting Started, you'll gain the foundational skills needed to analyze networks using Python. First, you'll learn about the origins of network science and its relation to graph theory, as well as practical skills in manipulating graphs in NetworkX. Next, you'll explore how to create beautiful and illustrative visualizations of networks using the native capabilities of NetworkX and Bokeh. Then, you'll deep dive into centrality and community detection algorithms. Finally, you'll enrich your machine learning toolbox by learning about network embeddings. By the end of the course, you'll have learned how to conduct your own analysis of networks, how to visualize networks, and even how to build an advanced friendship prediction engine using network science and machine learning.
Level
• Beginner
About Author
Artur is a Data Scientist helping fight financial crime at Revolut.
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