Graph-Powered Machine Learning

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.

Summary
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!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.

What's inside

Graphs in big data platforms
Recommendations, natural language processing, fraud detection
Graph algorithms
Working with the Neo4J graph database

About the reader
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.

Table of Contents
PART 1 INTRODUCTION
1 Machine learning and graphs: An introduction
2 Graph data engineering
3 Graphs in machine learning applications
PART 2 RECOMMENDATIONS
4 Content-based recommendations
5 Collaborative filtering
6 Session-based recommendations
7 Context-aware and hybrid recommendations
PART 3 FIGHTING FRAUD
8 Basic approaches to graph-powered fraud detection
9 Proximity-based algorithms
10 Social network analysis against fraud
PART 4 TAMING TEXT WITH GRAPHS
11 Graph-based natural language processing
12 Knowledge graphs

使用基於圖形的演算法來升級您的機器學習模型，這是處理複雜且相互關聯數據的完美結構。

摘要：
在《圖形驅動的機器學習》中，您將學到：
- 機器學習專案的生命周期
- 大數據平台中的圖形
- 使用圖形進行數據源建模
- 基於圖形的自然語言處理、推薦系統和詐騙檢測技術
- 圖形演算法
- 使用Neo4J進行工作

《圖形驅動的機器學習》教授使用基於圖形的演算法和數據組織策略來開發優秀的機器學習應用。您將深入研究圖形在機器學習和大數據平台中的角色，並詳細了解數據源建模、演算法設計、推薦系統和詐騙檢測。通過實際案例和基於作者Alessandro Negro與真實客戶合作的具體情境，您將從每一章中獲得作者豐富的經驗。

購買印刷版書籍將包含Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術：
識別關係是機器學習的基礎。通過識別和分析數據中的連接，像K最近鄰或PageRank這樣的以圖形為中心的演算法可以顯著提高機器學習應用的效果。基於圖形的機器學習技術為社交網絡、詐騙檢測、自然語言處理和推薦系統等領域提供了一個強大的新視角。

關於本書：
《圖形驅動的機器學習》教授您如何利用結構化和非結構化數據中的自然關係，使用以圖形為導向的機器學習演算法和工具。在這本權威的書籍中，您將掌握圖形的架構和設計實踐，並避免常見的問題。作者Alessandro Negro通過連接GraphML概念與真實世界任務的實例，探索了來自實際應用的例子。

內容簡介：
- 大數據平台中的圖形
- 推薦系統、自然語言處理、詐騙檢測
- 圖形演算法
- 使用Neo4J圖形數據庫進行工作

讀者對象：
對機器學習基礎知識感到舒適的讀者。

關於作者：
Alessandro Negro是GraphAware的首席科學家。他曾在許多會議上擔任演講嘉賓，並擁有計算機科學博士學位。

目錄：
第1部分：介紹
1. 機器學習和圖形：一個介紹
2. 圖形數據工程
3. 圖形在機器學習應用中的應用

第2部分：推薦系統
4. 基於內容的推薦系統
5. 協同過濾
6. 基於會話的推薦系統
7. 上下文感知和混合推薦系統

第3部分：打擊詐騙
8. 基於圖形的詐騙檢測的基本方法
9. 基於接近度的演算法
10. 社交網絡分析對抗詐騙

第4部分：利用圖形處理文本
11. 基於圖形的自然語言處理
12. 知識圖形

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.

Alessandro Negro是GraphAware的首席科學家。他在軟體開發、軟體架構和資料管理方面擁有豐富的經驗，並曾在許多會議上擔任演講嘉賓，如Java One、Oracle Open World和Graph Connect。他擁有計算機科學博士學位，並撰寫了幾篇關於基於圖形的機器學習的論文。