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所在平台: Udemy |
课程主页: https://www.udemy.com/course/knowledge-graph-with-neo4j-cypher-gds/
课程评论：没有评论
课程名称：Neo4j：Cypher、GDS、GraphQL、LLM和知识图谱用于RAG 课程概述： 欢迎参加“使用Neo4j、Cypher和GDS的知识图谱”课程！本课程将引领您掌握图数据库的强大世界，这是正在改变我们处理复杂数据关系的前沿技术。课程适合数据爱好者、开发人员及任何热衷于探索数据技术前沿的人士，帮助您使用Neo4j构建和查询强大的知识图谱。 课程内容将从Neo4j入门开始，探讨图数据库的独特之处及其在现代数据挑战中的重要性，了解Neo4j的架构及核心功能，为您的学习奠定坚实基础。接下来，通过真实案例研究，您将看到Neo4j如何在金融、医疗等各个行业中革新应用。课程还会阐明Neo4j在各种数据库类型中的定位，帮助您理解其在数据生态系统中的独特角色。 深入探讨属性图模型，它是Neo4j的核心，仅此模型便能有效表示复杂的连接数据。课程还包括在Windows上设置和安装Neo4j的实践实验，您将学会如何启动Neo4j，探索Neo4j浏览器，并设置初始数据集。还会涉及不同的Neo4j设置选项，包括云端、本地或混合部署。 接下来，我们会学习Cypher查询语言，它是Neo4j强大而直观的查询语言。通过一系列的实践实验，您将掌握Cypher的基本语法，逐步深入到过滤技巧、聚合、CRUD操作及更高级的功能，比如MERGE、WITH和RETURN。在最短路径实验中，您将学会如何在节点之间找到最快的路径。 课程还包括“使用Neo4j进行犯罪调查”的实际案例，运用您所学知识解决一个神秘案件。接着，我们将了解图数据科学库，并通过航班数据用例探索Neo4j图数据科学库中的强大算法，包括中心性、社区检测、节点相似性和路径寻找。 为了确保图数据库的高性能，我们将讨论Neo4j中的内存分配建议和写优化查询的最佳实践。此外，我们也会探讨Neo4j与AI集成的最新趋势，包括如何利用大型语言模型从非结构化数据中提取实体，并将其转化为知识图谱。 在课程结束时，您将拥有对Neo4j、Cypher和图数据科学库的全面理解，能够自信而熟练地构建、查询和优化自己的知识图谱。加入我们，迈向成为Neo4j及图数据库专家的第一步！ 请注意：本课程不涵盖Python编程基础，只在第8和第9节的实验中需要具备Python知识。
Kindly note: 1. Demos are recorded on Windows only.2. This course includes mostly practical use cases, datasets, and queries that are available on the official Neo4j Sandbox website. The objective is to guide you through these complex Cypher queries & concepts in an easy and time-efficient manner.3. This course does not cover the basics of Python programming. 3. Python knowledge is required (only for the labs in Sections 8 and 9) Course Update:Nov 2024 - Two New sections added:Section 8: Iteracting Neo4j from Python ProgramSection 9: Emerging Trends in Neo4j and AI Integration: LLMs and GraphRAG Welcome to "Knowledge Graph with Neo4j, Cypher, and GDS"! This comprehensive course is your gateway to mastering the powerful world of graph databases, a cutting-edge technology reshaping how we handle complex data relationships. Designed for data enthusiasts, developers, and anyone keen on exploring the frontier of data technology, this course will equip you with the skills to build and query robust knowledge graphs using Neo4j.We begin our journey with an Introduction to Neo4j, diving into what makes graph databases unique and essential for modern data challenges. You'll learn about the architecture and core features of Neo4j, setting a solid foundation for your learning.Next, we delve into Industry Applications of Neo4j. Through real-world case studies, you'll see how Neo4j is revolutionizing various industries, from finance to healthcare, showcasing its versatility and impact.Understanding where Neo4j fits in the data ecosystem is crucial, so we'll explore Where Neo4j Fits Among Various Database Types, helping you grasp its unique role compared to traditional databases.We then focus on the Property Graph Model, the backbone of Neo4j, explaining its components and why it's perfect for representing complex, connected data.Our hands-on labs start with Neo4j Setup and Installation on Windows. You'll learn how to get Neo4j up and running, explore the Neo4j Browser, and set up your initial dataset. We'll also cover different Options for Setting Up Neo4j, whether on the cloud, on-premises, or hybrid setups.The power of querying is unlocked with an Introduction to Cypher Query Language, Neo4j's expressive and powerful query language. You'll master Cypher through a series of practical labs, starting with the General Syntax of Cypher and moving to more advanced topics like Filtering Techniques, Aggregation, CRUD Operations, and advanced features like MERGE, WITH, and RETURN.In our Shortest Path lab, you'll learn how to find the quickest route between nodes, a fundamental skill in graph analytics.We then present an exciting challenge with our Crime Investigation Using Neo4j use case, where you'll apply what you've learned to solve a mystery.But the learning doesn't stop there! We move on to Understanding the Graph Data Science Library with an engaging Flights Data Use Case. Here, you'll explore powerful algorithms in Neo4j's Graph Data Science Library through hands-on labs, including Centrality, Community Detection, Node Similarity, and Path Finding.Performance is critical in graph databases, so we'll cover Memory Allocation Recommendations in Neo4j and share Best Practices to Write Optimized Queries, ensuring your queries are efficient and your databases run smoothly.We will also cover some emerging trends in Neo4j integration with AI. Here, we'll explore what Large Language Models are and how they can be used to extract entities from unstructured data and convert them into a knowledge graph. We'll understand this end-to-end use case with the help of Python code. Please note, this course does not teach you the basics of Python. In the end, we will cover some advanced topics like Retrieval-Augmented Generation and GraphRAG, and understand how these techniques can be used to create better context for LLMs.By the end of this course, you'll have a thorough understanding of Neo4j, Cypher, and the Graph Data Science Library. You'll be ready to build, query, and optimize your own knowledge graphs with confidence and expertise. Join us and take the first step towards becoming a Neo4j and graph database expert!
