Embark on a journey into the world of graph data science with our cutting-edge course on Neo4j Graph Data Science and LLM. This course is designed for professionals and enthusiasts eager to master the intricacies of graph databases and leverage the power of Large Language Models (LLM) in data science. Through a carefully structured curriculum, participants will explore the fundamentals of Neo4j, the leading graph database technology, and its application in modeling, analyzing, and visualizing complex relationships in vast datasets. You'll learn how to harness the capabilities of Neo4j to uncover deep insights and patterns that traditional data analysis methods might miss.

Building on this foundation, the course further delves into the integration of Neo4j with advanced LLM techniques, opening new avenues for natural language processing, recommendation systems, and artificial intelligence applications. Participants will gain hands-on experience working on real-world projects, where they will apply graph data science principles to solve practical problems and enhance AI models with the nuanced understanding that graph databases provide. Whether you're looking to boost your data science career or implement cutting-edge technologies in your projects, this course will equip you with the skills and knowledge to succeed in the fast-evolving landscape of graph data science and LLM.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant

HRD Corp Claimable Course for Employers Registered with HRD Corp

Course Code: M655

Topic 1 Introduction to Neo4J Graph Data Science

• Overview of Neo4j Graph Data Science (GDS)

• How GDS Works

• Graph Catalog

• Cypher Projections

Topic 2 Graph Algorithms

• Path Finding

• Community Detection

• Node Embedding

• Similarity

• Shortest Paths with Cypher

• Weighted Shortest Paths

Topic 3 Graph Machine Learning

• Overview of Graph Machine Learning

• Node Classification Pipeline

• Link Prediction

• Exploratory Analysis

• Handling Missing Values

• Encoding Categorical variables

• Dimensionality reduction

• KMeans algorithm

• Feature normalization

• Optimizing KMeans algorithm

• Nearest neighbor graph

• kNN algorithm

Topic 4 Neo4j and LLM

• Introduction to Neo4j with Generative AI

• Avoiding Hallucination

• Grounding LLMs

• Vectors & Semantic Search

• Vector Indexes

• Introduction to Langchain

• Large Lauguage Models (LLM)

• Chains

• Memory

• Agents

• Retrievers

• Using LLMs for Query Generation

• The Cypher QA Chain

• Conversational Agent