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[FreeCoursesOnline.io] MANNING - Graph-Powered Machine Learning [Video Edition]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	0de85e2eb7e10c2a64eb479f64f61eee47b23b48
Size:	4.8 GB in 86 files
Release Date(s):	Torrent Release: 21-05-2022 by user
Swarm:	0 Seeds & 0 Peers
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DESCRIPTION

Lynda and other Courses >>> https://freecoursesonline.io/

Forum for discussion >>> https://1hack.us/

[MANNING] Graph-Powered Machine Learning [Video Edition] [FCO]

Author: Alessandro Negro

Language: English

Released: September 2021

Duration: 12h 35m

Publisher(s): Manning Publications

Course Source: https://www.oreilly.com/library/view/graph-powered-machine-learning/9781617295645AU/

Book Source: https://www.manning.com/books/graph-powered-machine-learning

Video Description

I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps.

Helen Mary Labao-Barrameda, Okada Manila

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.

About the book

Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.

About the audience

For readers comfortable with machine learning basics.

About the author

Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science.

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FILE LIST

Filename		Size
	0. Websites you may like/1. Get Free Premium Accounts Daily On Our Discord Server!.txt	1.3 KB
	01-Part 1 Introduction.mp4	21.3 MB
	02-Chapter 1 Machine learning and graphs - An introduction.mp4	69.7 MB
	03-Chapter 1 Business understanding.mp4	39.1 MB
	04-Chapter 1 Machine learning challenges.mp4	49.8 MB
	05-Chapter 1 Performance.mp4	53.1 MB
	06-Chapter 1 Graphs.mp4	33.3 MB
	07-Chapter 1 Graphs as models of networks.mp4	71.3 MB
	08-Chapter 1 The role of graphs in machine learning.mp4	73.8 MB
	09-Chapter 2 Graph data engineering.mp4	82 MB
	10-Chapter 2 Velocity.mp4	50.8 MB
	11-Chapter 2 Graphs in the big data platform.mp4	49.4 MB
	12-Chapter 2 Graphs are valuable for big data.mp4	43.2 MB
	13-Chapter 2 Graphs are valuable for master data management.mp4	75.7 MB
	14-Chapter 2 Graph databases.mp4	52.1 MB
	15-Chapter 2 Sharding.mp4	70.5 MB
	16-Chapter 2 Native vs. non-native graph databases.mp4	79.9 MB
	17-Chapter 2 Label property graphs.mp4	37.7 MB
	18-Chapter 3 Graphs in machine learning applications.mp4	65.9 MB
	19-Chapter 3 Managing data sources.mp4	77.4 MB
	20-Chapter 3 Detect a fraud.mp4	52.3 MB
	21-Chapter 3 Recommend items.mp4	63.6 MB
	22-Chapter 3 Algorithms.mp4	48.2 MB
	23-Chapter 3 Find keywords in a document.mp4	53.6 MB
	24-Chapter 3 Storing and accessing machine learning models.mp4	31.4 MB
	25-Chapter 3 Monitoring a subject.mp4	55.5 MB
	26-Chapter 3 Visualization.mp4	37.9 MB
	27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4	52.8 MB
	28-Part 2 Recommendations.mp4	148.9 MB
	29-Chapter 4 Content-based recommendations.mp4	67.5 MB
	30-Chapter 4 Representing item features.mp4	63.4 MB
	31-Chapter 4 Representing item features.mp4	60.2 MB
	32-Chapter 4 User modeling.mp4	33.6 MB
	33-Chapter 4 Providing recommendations.mp4	56.8 MB
	34-Chapter 4 Providing recommendations.mp4	66.3 MB
	35-Chapter 4 Providing recommendations.mp4	72.6 MB
	36-Chapter 5 Collaborative filtering.mp4	99 MB
	37-Chapter 5 Collaborative filtering recommendations.mp4	92.7 MB
	38-Chapter 5 Computing the nearest neighbor network.mp4	69 MB
	39-Chapter 5 Computing the nearest neighbor network.mp4	47.9 MB
	40-Chapter 5 Providing recommendations.mp4	53.8 MB
	41-Chapter 5 Dealing with the cold-start problem.mp4	40.2 MB
	42-Chapter 6 Session-based recommendations.mp4	61.8 MB
	43-Chapter 6 The events chain and the session graph.mp4	68.3 MB
	44-Chapter 6 Providing recommendations.mp4	81.3 MB
	45-Chapter 6 Session-based k-NN.mp4	63.6 MB
	46-Chapter 7 Context-aware and hybrid recommendations.mp4	67.6 MB
	47-Chapter 7 Representing contextual information.mp4	42.9 MB
	48-Chapter 7 Providing recommendations.mp4	85.9 MB
	49-Chapter 7 Providing recommendations.mp4	85.1 MB
	50-Chapter 7 Advantages of the graph approach.mp4	51.8 MB
	51-Chapter 7 Providing recommendations.mp4	38.6 MB
	52-Part 3 Fighting fraud.mp4	34.4 MB
	53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4	48.5 MB
	54-Chapter 8 Fraud prevention and detection.mp4	45.2 MB
	55-Chapter 8 The role of graphs in fighting fraud.mp4	47.1 MB
	56-Chapter 8 Warm-up - Basic approaches.mp4	55.5 MB
	57-Chapter 8 Identifying a fraud ring.mp4	46.9 MB
	58-Chapter 9 Proximity-based algorithms.mp4	69 MB
	59-Chapter 9 Distance-based approach.mp4	49.9 MB
	60-Chapter 9 Creating the k-nearest neighbors graph.mp4	52.1 MB
	61-Chapter 9 Identifying fraudulent transactions.mp4	82.6 MB
	62-Chapter 9 Identifying fraudulent transactions.mp4	32.5 MB
	63-Chapter 10 Social network analysis against fraud.mp4	79.6 MB
	64-Chapter 10 Social network analysis concepts.mp4	46.4 MB
	65-Chapter 10 Score-based methods.mp4	32.2 MB
	66-Chapter 10 Neighborhood metrics.mp4	45.9 MB
	67-Chapter 10 Centrality metrics.mp4	61.3 MB
	68-Chapter 10 Collective inference algorithms.mp4	50.6 MB
	69-Chapter 10 Cluster-based methods.mp4	65.7 MB
	70-Part 4 Taming text with graphs.mp4	24.5 MB
	71-Chapter 11 Graph-based natural language processing.mp4	57.7 MB
	72-Chapter 11 A basic approach - Store and access sequence of words.mp4	53.5 MB
	73-Chapter 11 NLP and graphs.mp4	80.5 MB
	74-Chapter 11 NLP and graphs.mp4	70 MB
	75-Chapter 12 Knowledge graphs.mp4	60.1 MB
	76-Chapter 12 Knowledge graph building - Entities.mp4	94.1 MB
	77-Chapter 12 Knowledge graph building - Relationships.mp4	68.6 MB
	78-Chapter 12 Semantic networks.mp4	38.4 MB
	79-Chapter 12 Unsupervised keyword extraction.mp4	52.9 MB
	80-Chapter 12 Unsupervised keyword extraction.mp4	35.9 MB
	81-Chapter 12 Keyword co-occurrence graph.mp4	50.6 MB
	82-Appendix A. Machine learning algorithms taxonomy.mp4	65.2 MB
	83-Appendix C Graphs for processing patterns and workflows.mp4	43.8 MB
	84-Appendix C Graphs for defining complex processing workflows.mp4	50.4 MB
	85-Appendix D. Representing graphs.mp4	40.5 MB

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