Graph Algorithms for Data Science, Video Edition
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9:44:41
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001. Part 1. Introduction to graphs.mp4
01:52
002. Chapter 1. Graphs and network science An introduction.mp4
15:37
003. Chapter 1. How to spot a graph-shaped problem.mp4
07:58
004. Chapter 1. Summary.mp4
01:49
005. Chapter 2. Representing network structure Designing your first graph model.mp4
14:09
006. Chapter 2. Network representations.mp4
08:43
007. Chapter 2. Designing your first labeled-property graph model.mp4
21:22
008. Chapter 2. Extracting knowledge from text.mp4
15:18
009. Chapter 2. Summary.mp4
02:05
010. Part 2. Network analysis.mp4
01:24
011. Chapter 3. Your first steps with Cypher query language.mp4
39:52
012. Chapter 3. Importing CSV files with Cypher.mp4
20:45
013. Chapter 3. Solutions to exercises.mp4
02:50
014. Chapter 3. Summary.mp4
02:17
015. Chapter 4. Exploratory graph analysis.mp4
04:54
016. Chapter 4. Aggregating data with Cypher query language.mp4
15:09
017. Chapter 4. Filtering graph patterns.mp4
09:27
018. Chapter 4. Counting subqueries.mp4
02:07
019. Chapter 4. Multiple aggregations in sequence.mp4
05:05
020. Chapter 4. Solutions to exercises.mp4
02:12
021. Chapter 4. Summary.mp4
01:32
022. Chapter 5. Introduction to social network analysis.mp4
22:06
023. Chapter 5. Introduction to the Neo4j Graph Data Science library.mp4
06:07
024. Chapter 5. Network characterization.mp4
21:30
025. Chapter 5. Identifying central nodes.mp4
13:23
026. Chapter 5. Solutions to exercises.mp4
01:45
027. Chapter 5. Summary.mp4
02:26
028. Chapter 6. Projecting monopartite networks.mp4
17:38
029. Chapter 6. Retweet network characterization.mp4
11:29
030. Chapter 6. Identifying the most influential content creators.mp4
05:23
031. Chapter 6. Solutions to exercises.mp4
01:23
032. Chapter 6. Summary.mp4
01:44
033. Chapter 7. Inferring co-occurrence networks based on bipartite networks.mp4
22:45
034. Chapter 7. Constructing the co-occurrence network.mp4
22:33
035. Chapter 7. Characterization of the co-occurrence network.mp4
04:43
036. Chapter 7. Community detection with the label propagation algorithm.mp4
05:30
037. Chapter 7. Identifying community representaties with PageRank.mp4
05:44
038. Chapter 7. Solutions to exercises.mp4
01:00
039. Chapter 7. Summary.mp4
02:24
040. Chapter 8. Constructing a nearest neighbor similarity network.mp4
22:55
041. Chapter 8. Constructing the nearest neighbor graph.mp4
07:02
042. Chapter 8. User segmentation with the community detection algorithm.mp4
03:47
043. Chapter 8. Solutions to exercises.mp4
01:23
044. Chapter 8. Summary.mp4
01:31
045. Part 3. Graph machine learning.mp4
01:37
046. Chapter 9. Node embeddings and classification.mp4
12:47
047. Chapter 9. Node classification task.mp4
10:36
048. Chapter 9. The node2ec algorithm.mp4
24:12
049. Chapter 9. Solutions to exercises.mp4
00:14
050. Chapter 9. Summary.mp4
01:20
051. Chapter 10. Link prediction.mp4
13:46
052. Chapter 10. Dataset split.mp4
15:24
053. Chapter 10. Network feature engineering.mp4
16:28
054. Chapter 10. Link prediction classification model.mp4
07:48
055. Chapter 10. Solutions to exercises.mp4
00:55
056. Chapter 10. Summary.mp4
02:32
057. Chapter 11. Knowledge graph completion.mp4
16:23
058. Chapter 11. Knowledge graph completion.mp4
15:01
059. Chapter 11. Solutions to exercises.mp4
00:35
060. Chapter 11. Summary.mp4
01:33
061. Chapter 12. Constructing a graph using natural language processing techniques.mp4
09:44
062. Chapter 12. Named entity recognition.mp4
03:14
063. Chapter 12. Relation extraction.mp4
02:50
064. Chapter 12. Implementation of information extraction pipeline.mp4
13:07
065. Chapter 12. Solutions to exercises.mp4
00:38
066. Chapter 12. Summary.mp4
01:35
067. Appendix. The Neo4j enironment.mp4
04:36
068. Appendix. Neo4j installation.mp4
04:38
069. Appendix. Neo4j Browser configuration.mp4
00:30
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- language english
- Training sessions 69
- duration 9:44:41
- Release Date 2024/07/26
