Neo4j: Cypher, GDS, GraphQL, LLM, Knowledge Graphs for RAG
Hands-on Course on Neo4j, Cypher, GDS, GraphQL, GraphRAG and Building knowledge graph from unstructured data using LLM.
Neo4j: Cypher, GDS, GraphQL, LLM, Knowledge Graphs for RAG udemy course
Hands-on Course on Neo4j, Cypher, GDS, GraphQL, GraphRAG and Building knowledge graph from unstructured data using LLM.
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 Program
Section 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!
