Ementorhub

What you'll learn

In-demand, job-ready skills in gen AI, NLP apps, and large language models in just 3 months.

How to tokenize and load text data to train LLMs and deploy Skip-Gram, CBOW, Seq2Seq, RNN-based, and Transformer-based models with PyTorch

How to employ frameworks and pre-trained models such as LangChain and Llama for training, developing, fine-tuning, and deploying LLM applications.

How to implement a question-answering NLP system by preparing, developing, and deploying NLP applications using RAG.

Skills you'll gain

Applied Machine Learning

Prompt Engineering

Jupyter

Generative AI Agents

Deep Learning

Document Management

Text Mining

Feature Engineering

Generative AI

Large Language Modeling

Artificial Neural Networks

Data Processing

Specialization - 7 course series

In the final course, you will complete a capstone project, applying what you have learned to develop a question-answering bot through a series of hands-on labs. You begin by loading your document from various sources, then apply text splitting strategies to enhance model responsiveness, and use watsonx for embedding. You’ll also implement RAG to improve retrieval and set up a Gradio interface to construct your QA bot. Finally, you will test and deploy your bot.

Gen AI Foundational Models for NLP & Language Understanding

Generative AI Language Modeling with Transformers

Generative AI Engineering and Fine-Tuning Transformers

Generative AI Advance Fine-Tuning for LLMs

Fundamentals of AI Agents Using RAG and LangChain

Project: Generative AI Applications with RAG and LangChain