Generative AI and NLP in Python Online Course

Generative AI and NLP in Python Online Course

About Generative AI and NLP in Python Online Course

The Generative AI and NLP in Python online course provides a comprehensive exploration of Natural Language Processing (NLP), from foundational techniques to advanced real-world applications. You'll start by setting up your environment and familiarizing yourself with essential tools. The course covers key concepts such as text classification, sentiment analysis, and vector databases, while guiding you through the use of advanced models like Huggingface's Transformers. Practical modules focus on prompt engineering, chain-of-thought reasoning, and fine-tuning machine learning models. By the end, you'll be equipped with the skills to implement and optimize NLP solutions in your professional projects, enhancing your expertise in data-driven decision-making.

Key Benefits

• Thorough coverage of NLP techniques, ranging from foundational concepts to advanced methodologies.
• In-depth exploration of cutting-edge machine learning frameworks, equipping learners with the latest tools and technologies.
• Practical application of theoretical knowledge to real-world scenarios, supported by a well-structured and progressive learning path.

Target Audience

This course is for individuals with a foundational understanding of Python programming who wish to expand their expertise in Natural Language Processing (NLP) and Artificial Intelligence (AI). It is particularly suited for technical professionals seeking to apply machine learning models to text data and develop AI-powered applications.

Learning Objectives

• Set up an NLP-compatible environment, including the installation and configuration of essential tools and libraries.
• Leverage Huggingface's Transformers to perform advanced text processing with cutting-edge models.
• Gain expertise in managing, querying, and utilizing vector databases for efficient data handling.
• Fine-tune and deploy machine learning models tailored to specific NLP tasks for enhanced performance.
• Implement pre-trained models for key NLP tasks such as text classification, named entity recognition, and more.
• Master sophisticated NLP strategies, including prompt engineering and chain-of-thought prompting techniques, to tackle complex problems.

Course Topics

The Generative AI and NLP in Python Online Course covers the following topics - 

Domain 1 - Course Introduction

• Course Scope Overview
• Instructor Introduction
• How to Navigate the Course
• Accessing Course Materials (Coding)
• System Setup Guide (101)
• System Setup for Coding

Domain 2 - Introduction to Natural Language Processing (NLP)

• NLP Overview
• Basics of Word Embeddings
• Sentiment Analysis: One-Hot Encoding (OHE) Introduction
• Sentiment OHE Implementation (Coding)
• Word Embeddings with Neural Networks (NN)
• GloVe for Word Embedding (Coding)
• GloVe: Identifying Closest Words (Coding)
• GloVe: Word Analogy (Coding)
• GloVe: Word Clustering (101)
• GloVe Word Implementation (Coding)
• Sentiment Analysis with Embedding (101)
• Sentiment Analysis with Embedding (Coding)
• Introduction to Transformers (101)

Domain 3 - Applying Huggingface for Pre-Trained Models

• Huggingface Overview (101)
• Using Pipelines for General Text Classification (101)
• Pipelines for Text Classification (Coding)
• Named Entity Recognition (NER) Overview (101)
• Named Entity Recognition (NER) (Coding)
• Question Answering System (101)
• Question Answering (Coding)
• Text Summarization (101)
• Text Summarization (Coding)
• Translation with Huggingface (101)
• Translation (Coding)
• Fill-Mask Method (101)
• Fill-Mask (Coding)
• Zero-Shot Text Classification (101)
• Zero-Shot Text Classification (Coding)

Domain 4 - Model Fine-Tuning

• Overview of Fine-Tuning (101)
• Exploratory Data Analysis (Coding)
• Simple Model Development (Coding)
• Fine-Tuning Models (101)
• Huggingface Trainer for Model Training (101)
• Fine-Tuning Model (Coding)
• Saving and Loading Models with Huggingface (Coding)

Domain 5 - Vector Databases

• Vector Databases Overview (101)
• Tokenization Basics (101)
• Practical Tokenization (Coding)
• Building a Bible Vector Database (Full Picture)
• Data Preparation for Bible Vector DB (Coding)
• Database Management for Bible Vector DB (Coding)
• Exercises: Movies Vector Database (Coding)
• Data Prep for Movies Vector DB (Coding)
• Setup and Query Function for Movies Vector DB (Coding)
• Multimodal Vector Databases Overview (101)
• Multimodal Vector DB Setup (Coding)
• Querying Multimodal Vector DB (Coding)

Domain 6 - Working with OpenAI API

• OpenAI API Introduction (101)
• Obtaining Your API Key (Coding)
• Working with Python Package for OpenAI (101)
• Implementing Python Package for OpenAI (Coding)
• REST APIs and OpenAI WebUI (Coding)
• Understanding API Costs (101)

Domain 7 - Prompt Engineering

• Introduction to Prompt Engineering (101)
• Crafting Clear Instructions (Coding)
• Using Personas in Prompts (Coding)
• Defining Delimiters in Prompts (Coding)
• Dividing Tasks into Sub-Tasks (Coding)
• Providing Examples in Prompts (Coding)
• Controlling Output in Prompts (Coding)

Domain 8 - Advanced Prompt Engineering

• Advanced Prompt Techniques (101)
• Few-Shot Prompting (101)
• Chain-of-Thought Process (101)
• Example of Chain-of-Thought (Coding)
• Self-Consistency in Chain-of-Thought (101)
• Self-Consistency Example (Coding)
• Prompt Chaining (101)
• Example of Prompt Chaining (Coding)
• Reflection Techniques (101)
• Tree-of-Thought Methodology (101)
• Self-Feedback and Critique (101)
• Self-Critique Techniques (Coding)

Domain 9 - Retrieval-Augmented Generation (RAG)

• Introduction to RAG (101)
• RAG Coding: Final Results (Coding)
• Handling Vector Databases in RAG (Coding)
• Managing Large Language Models (LLMs) in RAG (Coding)
• Putting RAG Concepts Together (Coding)

Domain 10 - Capstone Project: Chatbot Development

• Overview of Climate Change Chatbot Webapp (101)
• Data Preparation for Chatbot (Coding)
• Implementing Vector Database for Chatbot (Coding)
• Integrating RAG in the Chatbot (Coding)
• Building the Final Chatbot Web Application (Coding)

Domain 11 - Open Source Large Language Models (LLMs)

• Open Source LLMs Overview (101)
• Implementing Open Source LLMs (Coding)

Domain 12 - Data Augmentation

• Introduction to Data Augmentation (101)
• Back-Translation Method (Coding)
• Synonym Replacement for Data Augmentation (Coding)
• Random Cropping for Data Augmentation (Coding)
• Contextual Augmentation Techniques (Coding)
• Word Embedding for Data Augmentation (Coding)
• Using Fill-Mask for Data Augmentation (Coding)