➢ Emergence of ChatGPT

➢ What is ChatGPT? History of ChatGPT

➢ How does ChatGPT work?

➢ Applications ofChatGPT

➢ Google vs ChatGPT

➢ Introduction to OpenAI and its Role in NLP and AI

➢ Overview of OpenAI's GPT models (e.g., GPT-2 and GPT-3)

➢ Environment setup

➢ Sign up for an OpenAI API account

➢ Using ChatGPT for live coding

➢ Build, optimize, and scale business using ChatGPT

➢ Advanced SEO for digital marketers

➢ Creating social media posts with ChatGPT

➢ Using ChatGPT for language translation

➢ Using ChatGPT for YouTube scripts

➢ Code generation and code debugging with ChatGPT

➢ Content creation with ChatGPT

➢ Question answering

➢ Sentiment analysis

➢ Building web development architecture

➢ Building backend server

➢ Setting up the database

➢ Setting up a React-based client-side application

➢ Writing user API requests to MongoDB with Express andReact

➢ Fetching and updating the database with MongoDB API and routing with Express

➢ Routing to React-based client-side application

➢ Debugging and client-side coding

➢ Create serverless ChatGPT

➢ Integrate ChatGPT with Power Automate

➢ Integrate ChatGPT with Power Apps

➢ Integrate ChatGPT with Outlook

➢ Integrate ChatGPT with Bubble

➢ Integrate ChatGPT with Airtable

➢ Deployment on cloud platforms

➢ Introduction to GPT models

➢ GPT-1, GPT-2, and GPT-3 models

➢ How GPT models work

➢ Limitations of GPT models

➢ Comparison with other language models (e.g., BERT, ELMo)

➢ Text pre-processing techniques (e.g., tokenization, stemming, stop word removal)

➢ Data cleaning and normalization

➢ Data augmentation techniques

➢ Handling missing data

➢ Encoding text data for machine learning models

➢ Introduction to fine-tuning

➢ Preparing data for fine-tuning

➢ Choosing the right hyperparameters

➢ Evaluating fine-tuned models

➢ Fine-tuning for specific applications (e.g., chatbots, language translation, sentiment analysis)

➢ Using ChatGPT for Communication

➢ Using ChatGPT to Write Social Media Posts

➢ Using ChatGPT to Write Blog Articles

➢ Using ChatGPT to Generate a Book Outline

➢ Using ChatGPT to Write Podcast Scripts

➢ Using ChatGPT for Career Advancement

➢ Using ChatGPT to Improve Your Resume

➢ Using ChatGPT to Write Job Search Emails

➢ Using ChatGPT to Write Tailored Cover Letters   

➢ Using ChatGPT for Search Engine Optimization (SEO)

➢ Using ChatGPT to Write Job Search Emails

➢ Using ChatGPT to Prepare for Job Interviews

➢ Introduction to Basic Coding

➢ Using ChatGPT to Display 'Hello World' in Different Coding Languages

➢ Using ChatGPT to Explain What

➢ Code Does and Create Code Comments

➢ Using ChatGPT to Create a Basic Webpage in HTML

➢ Using ChatGPT to Style Your Webpage

➢ Introduction

➢ Using ChatGPT to Overcome Technical Issues

➢ Using ChatGPT to Overcome Compatibility Issues

➢ Using ChatGPT to Strengthen Security

➢ Using ChatGPT to Navigate Complex Software and Applications

➢ Using ChatGPT to Translate Technical Jargon

➢ Cracking the Code - Introduction

➢ Translating Code to Human-Readable Text

➢ Using ChatGPT for Code Completion

➢ Using ChatGPT for Debugging Code

➢ Translating Code from One Language to Another

➢ Using ChatGPT for Regular Expression – Regex

➢ Introduction about Prompt Engineering and its design

➢ ChatGPT Prompt Engineering and DesignOverview

➢ Going Deeper with Prompts – Effective Prompt Design with the Help of ChatGPT

➢ Concise vs. Verbose Spectrum of Responses

➢ Mass Prompting in ChatGPT

➢ Introduction to LLM (Large Language Models)

➢ Application through Production

➢ Foundation Models from the Ground Up   

➢ Introduction to Neutral Networks

➢ Uses of Netural Netwotks in ChatGPT

➢ Introducing the Playground

➢ Understanding semantic search

➢ Understanding APIs

➢ Getting familiar with HTTP

➢ Reviewing the OpenAI APIendpoints

➢ Introducing CURL and Postman

➢ Understanding API authentication

➢ Making an authenticated request to the OpenAI API

➢ Introducing JSON

➢ Using the Completions endpoint

➢ Using the Semantic Search endpoint

➢ Working with GPT-3 API

➢ Implementing ChatGPT API

➢ Introduction to GPT-3 and its capabilities

➢ Democratizing NLP

➢ Understanding prompts, completions, and tokens

➢ Understanding GPT-3 risks

➢ Understanding general GPT-3 use cases

➢ Content filtering

➢ Sentiment analysis using GPT-3

➢ Text summarization using GPT-3

➢ Question answering and information retrieval

➢ Handling text generation and classification tasks

➢ Advanced ChatGPT Development Techniques Introduction

➢ Using ChatGPT to Generate Sample Data

➢ Using ChatGPT to Create a Python Fake Data Generator

➢ Generating Website Ideas with ChatGPT

➢ Building Webpages in HTML with ChatGPT

➢ Integrating ChatGPT with Visual Studio Code

➢ Creating Algorithms with ChatGPT

➢ Creating Batch Apex with ChatGPT

➢ Using ChatGPT to Write a SQL Query

➢ Using ChatGPT to Create Games in Python

➢ Introduction to building and deploying GPT-3 powered applications

➢ Overview of GPT-3 APIs and SDKs

➢ Choosing the right programming language and environment for your application

➢ Setting up the GPT-3 API and integrating it into projects

➢ Building conversational AI for finance and e-commerce domain

➢ Strengths and limitations of GPT-3 in building conversational AI

➢ Monitoring and scaling the application

➢ Building custom prompts and fine-tuning GPT-3 models for your application

➢ Security and privacy considerations when working with GPT-3

➢ Best practices for building and deploying GPT-3 powered applications

➢ Scaling and deploying GPT-3 models to production

➢ Build and deploy the ChatGPT AI app

➢ Build a diet planning application

➢ Build a website and create landing page content using ChatGPT

➢ Data cleaning and preparation techniques

➢ Choosing the right hyperparameters

➢ Evaluating model performance

➢ Using pre-trained models and fine-tuning

➢ Managing bias and ethical considerations

➢ Creating diverse and representative training data

➢ Building user-friendly interfaces for chatbots and virtual assistants

➢ Handling user data and privacy concerns

➢ Debugging and troubleshooting common errors

➢ Continuous model monitoring and updating.

➢ Training data quality and quantity

➢ Generalization and transfer learning

➢ Understanding and mitigating modelbias

➢ Explainability and interpretability

➢ Scalability and speed of inference

➢ Support for low-resource languages and dialects

 ➢ Interoperability with other models and APIs

➢ Adversarial attacks and security concerns

➢ Availability and accessibility of computational resources.

➢ Multi-modal models for text, image, and audioprocessing

➢ Multi-task and meta-learning approaches

➢ Advanced transfer learning and self-supervised techniques

➢ Integrating knowledge graphs and semantic web technologies

➢ Addressing ethical and social issues in natural languageprocessing

Designing models for personalized and adaptive u , Fintech AI Integration , Lab Project