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Graph-Powered Machine Learning: machine learning done better

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At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.

Take 42% off the book. Just enter slnegro into the discount code box at checkout. More information about the book can be found here: http://bit.ly/2Q7sPg3

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Graph-Powered Machine Learning: machine learning done better

  1. 1. Develop Superior Machine Learning Algorithms With Graph-Powered Machine Learning. Take 42% off the book. Just enter code slnegro into the discount code box at checkout at manning.com.
  2. 2. Learn the role of graphs in machine learning Graphs are an incredibly powerful tool for any task that involves pattern matching in large data sets—like machine learning. Graph-Powered Machine Learning teaches you how to use graph- based algorithms and data organization strategies to develop superior machine learning applications.
  3. 3. Find patterns in complex data Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. And modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning.
  4. 4. Go in depth into the technology Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in- depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. Learning curves for confusion set disambiguation
  5. 5. Learn from a leading expert Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning. Property graph
  6. 6. If you want to learn more about the book, check it out on liveBook here. Get Graph-Powered Machine Learning for 42% off. Just enter code slnegro into the discount code box at checkout at manning.com.

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