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Building custom NER for medical domain

Instructor based workshop for creating custom domain based Named Entity recognizer (NER) model.

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4.7

Rating

10 Hrs

Duration

Advance

Level

10

Assignments

What you'll learn

How to prepare training data set for NER models
BERT and transformer-based architecture for training custom NER models
Developing advance NER solution using LLM and Knowledge graph

Who should take this course

Ideal for anyone with python programming and Machine learning and NLP knowledge
Anyone who want to create custom entities that are not available in open-source NER models

Skills you'll gain

Python
NER
Entity identification
Text embeddings
Ontology based model
NLP
Machine learning
Deep Learning
BERT

Course contents

Understanding the use case objective

Text preprocessing
Annotating the entities

Using Transformer architecture
Annotating the entities

Create a demo app in python streamlit
Identified entities will be highlighted in colour

Calculating the performance metrics of the output

Using LLM and Knowledge Graph

Creating Graph DB AI agents for applying GraphRAG

This workshop focuses on how to enhances the performance of RAG LLM by adding external knowledge base.

Building Knowledge Graph from documents

Here we will built a pipeline to convert unstructured text data into a graph representation of knowledge base.

Fine tuning Embedding model for domain specific search

In this course we will fine-tune a BERT models with medical data that can understand medical terminology and answer medical questions more accurately.