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Neo4j Graph Data Science and LLM

Course Information

  • Sessions 2 days
  • Duration 15 hrs
  • Level Intermediate
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Suite Pejabat Korporat, KL Gateway, No 2, Jalan kerinchi, Gerbang kernichi Lestari, 59200 Kuala Lumpur, Malaysia
Penang: Jalan Sungai Dua, 11700 Penang, Malaysia.

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Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.

Neo4j Graph Data Science and LLM

Course Code: C640
  • HRDF

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What's This Course About

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.

WSQ Funding

Full Fee 1,800.00 Before GST
GST 162.00 9% of fee
Baseline Nett 1,062.00 SG/PR age 21+ · 50% funded
MCES / SME Nett 702.00 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - Neo4j Graph Data Science and Large Language Model (LLM)

Course Fee

MYR1,800.00

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

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

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • Data Scientist specializing in Graph Databases
  • Neo4j Database Administrator
  • Graph Data Analyst
  • AI and Machine Learning Engineer
  • Natural Language Processing Engineer
  • Graph Database Consultant
  • Data Integration Specialist
  • Business Intelligence Analyst
  • Network Analysis Researcher
  • Recommendation Systems Developer
  • AI Application Developer
  • Data Architect specializing in Graph Technologies
  • Cybersecurity Analyst using Graph Data Science
  • Financial Analyst leveraging Graph Databases
  • Healthcare Data Analyst
  • Social Network Analyst
  • Supply Chain Optimization Analyst
  • Graph Algorithm Developer
  • Software Developer with Neo4j Expertise
  • Academic Researcher in Graph Theory and Data Science

Trainers

Trainers

Saeid is co-founder of Skymics Sdn Bhd. He has 8 years of experience in the field of IoT (Internet of Things) and Information Technology. He is a certified IBM IoT Practitioner and instructor, and a Certified Citizen Data Scientist Train-The-Trainer. He has been co-inventor of 3 inventions during the last 4 years.

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