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01-Part 1 Introduction.mp4
21.31MB
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02-Chapter 1 Machine learning and graphs - An introduction.mp4
69.7MB
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03-Chapter 1 Business understanding.mp4
39.1MB
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04-Chapter 1 Machine learning challenges.mp4
49.84MB
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05-Chapter 1 Performance.mp4
53.14MB
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06-Chapter 1 Graphs.mp4
33.32MB
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07-Chapter 1 Graphs as models of networks.mp4
71.29MB
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08-Chapter 1 The role of graphs in machine learning.mp4
73.83MB
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09-Chapter 2 Graph data engineering.mp4
82.01MB
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10-Chapter 2 Velocity.mp4
50.81MB
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11-Chapter 2 Graphs in the big data platform.mp4
49.38MB
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12-Chapter 2 Graphs are valuable for big data.mp4
43.18MB
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13-Chapter 2 Graphs are valuable for master data management.mp4
75.67MB
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14-Chapter 2 Graph databases.mp4
52.12MB
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15-Chapter 2 Sharding.mp4
70.52MB
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16-Chapter 2 Native vs. non-native graph databases.mp4
79.92MB
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17-Chapter 2 Label property graphs.mp4
37.69MB
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18-Chapter 3 Graphs in machine learning applications.mp4
65.87MB
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19-Chapter 3 Managing data sources.mp4
77.36MB
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20-Chapter 3 Detect a fraud.mp4
52.33MB
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21-Chapter 3 Recommend items.mp4
63.56MB
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22-Chapter 3 Algorithms.mp4
48.19MB
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23-Chapter 3 Find keywords in a document.mp4
53.6MB
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24-Chapter 3 Storing and accessing machine learning models.mp4
31.38MB
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25-Chapter 3 Monitoring a subject.mp4
55.54MB
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26-Chapter 3 Visualization.mp4
37.9MB
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27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4
52.78MB
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28-Part 2 Recommendations.mp4
148.91MB
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29-Chapter 4 Content-based recommendations.mp4
67.48MB
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30-Chapter 4 Representing item features.mp4
63.39MB
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31-Chapter 4 Representing item features.mp4
60.23MB
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32-Chapter 4 User modeling.mp4
33.57MB
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33-Chapter 4 Providing recommendations.mp4
56.79MB
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34-Chapter 4 Providing recommendations.mp4
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35-Chapter 4 Providing recommendations.mp4
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36-Chapter 5 Collaborative filtering.mp4
98.97MB
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37-Chapter 5 Collaborative filtering recommendations.mp4
92.75MB
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38-Chapter 5 Computing the nearest neighbor network.mp4
69.04MB
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39-Chapter 5 Computing the nearest neighbor network.mp4
47.87MB
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40-Chapter 5 Providing recommendations.mp4
53.76MB
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41-Chapter 5 Dealing with the cold-start problem.mp4
40.18MB
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42-Chapter 6 Session-based recommendations.mp4
61.79MB
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43-Chapter 6 The events chain and the session graph.mp4
68.35MB
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44-Chapter 6 Providing recommendations.mp4
81.3MB
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45-Chapter 6 Session-based k-NN.mp4
63.6MB
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46-Chapter 7 Context-aware and hybrid recommendations.mp4
67.6MB
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47-Chapter 7 Representing contextual information.mp4
42.88MB
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48-Chapter 7 Providing recommendations.mp4
85.94MB
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49-Chapter 7 Providing recommendations.mp4
85.12MB
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50-Chapter 7 Advantages of the graph approach.mp4
51.81MB
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51-Chapter 7 Providing recommendations.mp4
38.56MB
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52-Part 3 Fighting fraud.mp4
34.38MB
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53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4
48.49MB
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54-Chapter 8 Fraud prevention and detection.mp4
45.24MB
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55-Chapter 8 The role of graphs in fighting fraud.mp4
47.11MB
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56-Chapter 8 Warm-up - Basic approaches.mp4
55.49MB
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57-Chapter 8 Identifying a fraud ring.mp4
46.91MB
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58-Chapter 9 Proximity-based algorithms.mp4
68.99MB
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59-Chapter 9 Distance-based approach.mp4
49.88MB
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60-Chapter 9 Creating the k-nearest neighbors graph.mp4
52.11MB
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61-Chapter 9 Identifying fraudulent transactions.mp4
82.58MB
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62-Chapter 9 Identifying fraudulent transactions.mp4
32.51MB
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63-Chapter 10 Social network analysis against fraud.mp4
79.64MB
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64-Chapter 10 Social network analysis concepts.mp4
46.44MB
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65-Chapter 10 Score-based methods.mp4
32.24MB
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66-Chapter 10 Neighborhood metrics.mp4
45.87MB
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67-Chapter 10 Centrality metrics.mp4
61.27MB
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68-Chapter 10 Collective inference algorithms.mp4
50.6MB
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69-Chapter 10 Cluster-based methods.mp4
65.65MB
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70-Part 4 Taming text with graphs.mp4
24.45MB
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71-Chapter 11 Graph-based natural language processing.mp4
57.65MB
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72-Chapter 11 A basic approach - Store and access sequence of words.mp4
53.54MB
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73-Chapter 11 NLP and graphs.mp4
80.48MB
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74-Chapter 11 NLP and graphs.mp4
70.02MB
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75-Chapter 12 Knowledge graphs.mp4
60.09MB
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76-Chapter 12 Knowledge graph building - Entities.mp4
94.08MB
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77-Chapter 12 Knowledge graph building - Relationships.mp4
68.65MB
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78-Chapter 12 Semantic networks.mp4
38.36MB
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79-Chapter 12 Unsupervised keyword extraction.mp4
52.87MB
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80-Chapter 12 Unsupervised keyword extraction.mp4
35.89MB
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81-Chapter 12 Keyword co-occurrence graph.mp4
50.57MB
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82-Appendix A. Machine learning algorithms taxonomy.mp4
65.16MB
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83-Appendix C Graphs for processing patterns and workflows.mp4
43.83MB
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84-Appendix C Graphs for defining complex processing workflows.mp4
50.43MB
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85-Appendix D. Representing graphs.mp4
40.52MB
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