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[ CoursePig com ] Graph-Powered Machine Learning, Video Edition
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01-Part 1 Introduction.mp4
02-Chapter 1 Machine learning and graphs - An introduction.mp4
03-Chapter 1 Business understanding.mp4
04-Chapter 1 Machine learning challenges.mp4
05-Chapter 1 Performance.mp4
06-Chapter 1 Graphs.mp4
07-Chapter 1 Graphs as models of networks.mp4
08-Chapter 1 The role of graphs in machine learning.mp4
09-Chapter 2 Graph data engineering.mp4
10-Chapter 2 Velocity.mp4
11-Chapter 2 Graphs in the big data platform.mp4
12-Chapter 2 Graphs are valuable for big data.mp4
13-Chapter 2 Graphs are valuable for master data management.mp4
14-Chapter 2 Graph databases.mp4
15-Chapter 2 Sharding.mp4
16-Chapter 2 Native vs. non-native graph databases.mp4
17-Chapter 2 Label property graphs.mp4
18-Chapter 3 Graphs in machine learning applications.mp4
19-Chapter 3 Managing data sources.mp4
20-Chapter 3 Detect a fraud.mp4
21-Chapter 3 Recommend items.mp4
22-Chapter 3 Algorithms.mp4
23-Chapter 3 Find keywords in a document.mp4
24-Chapter 3 Storing and accessing machine learning models.mp4
25-Chapter 3 Monitoring a subject.mp4
26-Chapter 3 Visualization.mp4
27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4
28-Part 2 Recommendations.mp4
29-Chapter 4 Content-based recommendations.mp4
30-Chapter 4 Representing item features.mp4
31-Chapter 4 Representing item features.mp4
32-Chapter 4 User modeling.mp4
33-Chapter 4 Providing recommendations.mp4
34-Chapter 4 Providing recommendations.mp4
35-Chapter 4 Providing recommendations.mp4
36-Chapter 5 Collaborative filtering.mp4
37-Chapter 5 Collaborative filtering recommendations.mp4
38-Chapter 5 Computing the nearest neighbor network.mp4
39-Chapter 5 Computing the nearest neighbor network.mp4
40-Chapter 5 Providing recommendations.mp4
41-Chapter 5 Dealing with the cold-start problem.mp4
42-Chapter 6 Session-based recommendations.mp4
43-Chapter 6 The events chain and the session graph.mp4
44-Chapter 6 Providing recommendations.mp4
45-Chapter 6 Session-based k-NN.mp4
46-Chapter 7 Context-aware and hybrid recommendations.mp4
47-Chapter 7 Representing contextual information.mp4
48-Chapter 7 Providing recommendations.mp4
49-Chapter 7 Providing recommendations.mp4
50-Chapter 7 Advantages of the graph approach.mp4
51-Chapter 7 Providing recommendations.mp4
52-Part 3 Fighting fraud.mp4
53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4
54-Chapter 8 Fraud prevention and detection.mp4
55-Chapter 8 The role of graphs in fighting fraud.mp4
56-Chapter 8 Warm-up - Basic approaches.mp4
57-Chapter 8 Identifying a fraud ring.mp4
58-Chapter 9 Proximity-based algorithms.mp4
59-Chapter 9 Distance-based approach.mp4
60-Chapter 9 Creating the k-nearest neighbors graph.mp4
61-Chapter 9 Identifying fraudulent transactions.mp4
62-Chapter 9 Identifying fraudulent transactions.mp4
63-Chapter 10 Social network analysis against fraud.mp4
64-Chapter 10 Social network analysis concepts.mp4
65-Chapter 10 Score-based methods.mp4
66-Chapter 10 Neighborhood metrics.mp4
67-Chapter 10 Centrality metrics.mp4
68-Chapter 10 Collective inference algorithms.mp4
69-Chapter 10 Cluster-based methods.mp4
70-Part 4 Taming text with graphs.mp4
71-Chapter 11 Graph-based natural language processing.mp4
72-Chapter 11 A basic approach - Store and access sequence of words.mp4
73-Chapter 11 NLP and graphs.mp4
74-Chapter 11 NLP and graphs.mp4
75-Chapter 12 Knowledge graphs.mp4
76-Chapter 12 Knowledge graph building - Entities.mp4
77-Chapter 12 Knowledge graph building - Relationships.mp4
78-Chapter 12 Semantic networks.mp4
79-Chapter 12 Unsupervised keyword extraction.mp4
80-Chapter 12 Unsupervised keyword extraction.mp4
81-Chapter 12 Keyword co-occurrence graph.mp4
82-Appendix A. Machine learning algorithms taxonomy.mp4
83-Appendix C Graphs for processing patterns and workflows.mp4
84-Appendix C Graphs for defining complex processing workflows.mp4
85-Appendix D. Representing graphs.mp4
Bonus Resources.txt
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