The Nlp & Llm Crash Course - Build Your Ai Chatbot Quickly

Master NLP and Large Language Models (LLM): Build and deploy your own ChatGPT-like chatbot with Python in record time.

What you'll learn

Understand how NLP & LLMs and their architecture work

Implement Sentiment Analysis models

Implement Named Entity Recognition (NER) models

Implement Question-Answering models

Learn how to provision your own space with a GPU

Learn how to create a chatbot interface

Learn how to create your own AI chatbot from scratch in just 2 hours

Learn how to use Open Source models like Llama 3.1, BERT, and others

Requirements

Basic Knowlegde of Python

Description

Master NLP and Large Language Models (LLM): Build and deploy your ChatGPT-like chatbot with Python in record time.Would you like to dive into artificial intelligence and create your own chatbot in just 2 hours? This is possible with our intensive course on NLP & LLM and Generative AI. We will teach you from scratch what a Large Language Model (LLM) is and how to leverage its power to develop innovative applications.What You'll Learn:Natural Language Processing (NLP) & Large Language Models (LLMs): Understand the architecture and inner workings of LLMs like GPT.Transformers Library: Harness pipelines for sentiment analysis and entity recognition—key skills in natural language processing.AutoClass Models: Get hands-on with AutoModel and AutoTokenizer to build question-answering systems.Advanced Environments: Set up GPU configurations and create authentication tokens to work with sophisticated AI models.Build a User Interface for the Chatbot: Create an intuitive chat-style interface to test your chatbot.Open Source Models: Learn how to choose the right model based on the specific task at hand.Chatbot Development: Build the chat logic, design an engaging user interface, and deploy your very own LLM-powered chatbot.What You Need:All you need is a basic knowledge of Python and a computer to start building your own chatbot.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is an LLM?

Lecture 3 LLM Architecture

Lecture 4 Type of Tasks for LLMs

Section 2: Transformers

Lecture 5 What is the Transformers library?

Lecture 6 Link of the Notebook: Sentiment Analysis

Lecture 7 Using Pipeline for Sentiment Analysis

Lecture 8 How to search and pick an NLP model

Lecture 9 Link of the Notebook: Named Entity Recognition

Lecture 10 Using Pipeline for NER

Lecture 11 Link of the Notebook: Question & Answer

Lecture 12 Using AutoModel and AutoTokenizer for QA

Lecture 13 Generate and process the response of Q&A model

Section 3: LLM Chatbot Implementation

Lecture 14 Select the model to implement the chatbot

Lecture 15 Provisiong an space with GPU

Lecture 16 Create the Authentication Token

Lecture 17 Setup Llama model

Lecture 18 Process and tokenize the inputs

Lecture 19 Define terminators and generate the output of the model

Lecture 20 Create the UI of the chatbot

Lecture 21 Deploy your LLM chatbot

Lecture 22 Repository of our Chatbot

Section 4: Course Farewell

Lecture 23 Course Farewell

Everyone with basic knowledge of Python