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[ CoursePig.com ] Graph-Powered Machine Learning, Video Edition
TORRENT SUMMARY
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Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data
In Graph-Powered Machine Learning you will learn
The lifecycle of a machine learning project
Graphs in big data platforms
Data source modeling using graphs
Graph-based natural language processing, recommendations, and fraud detection techniques
Graph algorithms
Working with Neo4J
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients!
about the technology
Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems.
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FILE LIST
Filename
Size
~Get Your Files Here !/01-Part 1 Introduction.mp4
21.3 MB
~Get Your Files Here !/02-Chapter 1 Machine learning and graphs - An introduction.mp4
69.7 MB
~Get Your Files Here !/03-Chapter 1 Business understanding.mp4
39.1 MB
~Get Your Files Here !/04-Chapter 1 Machine learning challenges.mp4
49.8 MB
~Get Your Files Here !/05-Chapter 1 Performance.mp4
53.1 MB
~Get Your Files Here !/06-Chapter 1 Graphs.mp4
33.3 MB
~Get Your Files Here !/07-Chapter 1 Graphs as models of networks.mp4
71.3 MB
~Get Your Files Here !/08-Chapter 1 The role of graphs in machine learning.mp4
73.8 MB
~Get Your Files Here !/09-Chapter 2 Graph data engineering.mp4
82 MB
~Get Your Files Here !/10-Chapter 2 Velocity.mp4
50.8 MB
~Get Your Files Here !/11-Chapter 2 Graphs in the big data platform.mp4
49.4 MB
~Get Your Files Here !/12-Chapter 2 Graphs are valuable for big data.mp4
43.2 MB
~Get Your Files Here !/13-Chapter 2 Graphs are valuable for master data management.mp4
75.7 MB
~Get Your Files Here !/14-Chapter 2 Graph databases.mp4
52.1 MB
~Get Your Files Here !/15-Chapter 2 Sharding.mp4
70.5 MB
~Get Your Files Here !/16-Chapter 2 Native vs. non-native graph databases.mp4
79.9 MB
~Get Your Files Here !/17-Chapter 2 Label property graphs.mp4
37.7 MB
~Get Your Files Here !/18-Chapter 3 Graphs in machine learning applications.mp4
65.9 MB
~Get Your Files Here !/19-Chapter 3 Managing data sources.mp4
77.4 MB
~Get Your Files Here !/20-Chapter 3 Detect a fraud.mp4
52.3 MB
~Get Your Files Here !/21-Chapter 3 Recommend items.mp4
63.6 MB
~Get Your Files Here !/22-Chapter 3 Algorithms.mp4
48.2 MB
~Get Your Files Here !/23-Chapter 3 Find keywords in a document.mp4
53.6 MB
~Get Your Files Here !/24-Chapter 3 Storing and accessing machine learning models.mp4
31.4 MB
~Get Your Files Here !/25-Chapter 3 Monitoring a subject.mp4
55.5 MB
~Get Your Files Here !/26-Chapter 3 Visualization.mp4
37.9 MB
~Get Your Files Here !/27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4
52.8 MB
~Get Your Files Here !/28-Part 2 Recommendations.mp4
148.9 MB
~Get Your Files Here !/29-Chapter 4 Content-based recommendations.mp4
67.5 MB
~Get Your Files Here !/30-Chapter 4 Representing item features.mp4
63.4 MB
~Get Your Files Here !/31-Chapter 4 Representing item features.mp4
60.2 MB
~Get Your Files Here !/32-Chapter 4 User modeling.mp4
33.6 MB
~Get Your Files Here !/33-Chapter 4 Providing recommendations.mp4
56.8 MB
~Get Your Files Here !/34-Chapter 4 Providing recommendations.mp4
66.3 MB
~Get Your Files Here !/35-Chapter 4 Providing recommendations.mp4
72.6 MB
~Get Your Files Here !/36-Chapter 5 Collaborative filtering.mp4
99 MB
~Get Your Files Here !/37-Chapter 5 Collaborative filtering recommendations.mp4
92.7 MB
~Get Your Files Here !/38-Chapter 5 Computing the nearest neighbor network.mp4
69 MB
~Get Your Files Here !/39-Chapter 5 Computing the nearest neighbor network.mp4
47.9 MB
~Get Your Files Here !/40-Chapter 5 Providing recommendations.mp4
53.8 MB
~Get Your Files Here !/41-Chapter 5 Dealing with the cold-start problem.mp4
40.2 MB
~Get Your Files Here !/42-Chapter 6 Session-based recommendations.mp4
61.8 MB
~Get Your Files Here !/43-Chapter 6 The events chain and the session graph.mp4
68.3 MB
~Get Your Files Here !/44-Chapter 6 Providing recommendations.mp4
81.3 MB
~Get Your Files Here !/45-Chapter 6 Session-based k-NN.mp4
63.6 MB
~Get Your Files Here !/46-Chapter 7 Context-aware and hybrid recommendations.mp4
67.6 MB
~Get Your Files Here !/47-Chapter 7 Representing contextual information.mp4
42.9 MB
~Get Your Files Here !/48-Chapter 7 Providing recommendations.mp4
85.9 MB
~Get Your Files Here !/49-Chapter 7 Providing recommendations.mp4
85.1 MB
~Get Your Files Here !/50-Chapter 7 Advantages of the graph approach.mp4
51.8 MB
~Get Your Files Here !/51-Chapter 7 Providing recommendations.mp4
38.6 MB
~Get Your Files Here !/52-Part 3 Fighting fraud.mp4
34.4 MB
~Get Your Files Here !/53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4
48.5 MB
~Get Your Files Here !/54-Chapter 8 Fraud prevention and detection.mp4
45.2 MB
~Get Your Files Here !/55-Chapter 8 The role of graphs in fighting fraud.mp4
47.1 MB
~Get Your Files Here !/56-Chapter 8 Warm-up - Basic approaches.mp4
55.5 MB
~Get Your Files Here !/57-Chapter 8 Identifying a fraud ring.mp4
46.9 MB
~Get Your Files Here !/58-Chapter 9 Proximity-based algorithms.mp4
69 MB
~Get Your Files Here !/59-Chapter 9 Distance-based approach.mp4
49.9 MB
~Get Your Files Here !/60-Chapter 9 Creating the k-nearest neighbors graph.mp4
52.1 MB
~Get Your Files Here !/61-Chapter 9 Identifying fraudulent transactions.mp4
82.6 MB
~Get Your Files Here !/62-Chapter 9 Identifying fraudulent transactions.mp4
32.5 MB
~Get Your Files Here !/63-Chapter 10 Social network analysis against fraud.mp4
79.6 MB
~Get Your Files Here !/64-Chapter 10 Social network analysis concepts.mp4
46.4 MB
~Get Your Files Here !/65-Chapter 10 Score-based methods.mp4
32.2 MB
~Get Your Files Here !/66-Chapter 10 Neighborhood metrics.mp4
45.9 MB
~Get Your Files Here !/67-Chapter 10 Centrality metrics.mp4
61.3 MB
~Get Your Files Here !/68-Chapter 10 Collective inference algorithms.mp4
50.6 MB
~Get Your Files Here !/69-Chapter 10 Cluster-based methods.mp4
65.7 MB
~Get Your Files Here !/70-Part 4 Taming text with graphs.mp4
24.5 MB
~Get Your Files Here !/71-Chapter 11 Graph-based natural language processing.mp4
57.7 MB
~Get Your Files Here !/72-Chapter 11 A basic approach - Store and access sequence of words.mp4
53.5 MB
~Get Your Files Here !/73-Chapter 11 NLP and graphs.mp4
80.5 MB
~Get Your Files Here !/74-Chapter 11 NLP and graphs.mp4
70 MB
~Get Your Files Here !/75-Chapter 12 Knowledge graphs.mp4
60.1 MB
~Get Your Files Here !/76-Chapter 12 Knowledge graph building - Entities.mp4
94.1 MB
~Get Your Files Here !/77-Chapter 12 Knowledge graph building - Relationships.mp4
68.6 MB
~Get Your Files Here !/78-Chapter 12 Semantic networks.mp4
38.4 MB
~Get Your Files Here !/79-Chapter 12 Unsupervised keyword extraction.mp4
52.9 MB
~Get Your Files Here !/80-Chapter 12 Unsupervised keyword extraction.mp4
35.9 MB
~Get Your Files Here !/81-Chapter 12 Keyword co-occurrence graph.mp4
50.6 MB
~Get Your Files Here !/82-Appendix A. Machine learning algorithms taxonomy.mp4
65.2 MB
~Get Your Files Here !/83-Appendix C Graphs for processing patterns and workflows.mp4
43.8 MB
~Get Your Files Here !/84-Appendix C Graphs for defining complex processing workflows.mp4
50.4 MB
~Get Your Files Here !/85-Appendix D. Representing graphs.mp4
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