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Using ChatGPT in Python for NLP Applications

Course Summary

This course provides participants with the knowledge to leverage ChatGPT for AI-driven applications including chatbots, customer service, and content creation. Through hands-on activities, participants will gain an understanding of Large Language Models (LLMs) and how they work. and will integrate ChatGPT into various Natural Language Processing (NLP) applications. The course will also explore best practices, ethical considerations, and optimization techniques.

Prerequisites:

  • Basic proficiency in Python for scripting and API usage
  • Familiarity wiht Natural Language Processing (NLP) concepts including tokenization, sentiment analysis, and named entity recognition
  • Experience with machine learning principles including supervised and unsupervised learning
Purpose
Understand how ChatGPT generates natural language responses
Audience

This course was designed for:

Python developers interested in integrationg LLMs into their applications

Developers who have experience with other programming languages and want to apply Python for AI-powered applications

Role
Software Developers
Skill level
Intermediate
Style
Lecture | Hands-on Activities | Labs
Duration
5 days
Related technologies
Natural Language Processing | AI/ML | Python | Gen AI

 

Learning Objectives
  • Use OpenAI API to interact with ChatGPT in Python
  • Develop chatbots that handle multi-turn conversations and maintain context
  • Generate AI-powered content for text summarization, creative writing, and automation
  • Implement AI best practices to ensure ethical and responsible usage

What you'll learn:

In this Using ChatGPT in Python for NLP Applications course, you'll learn:

Introduction to ChatGPT

  • Overview of Generative AI ( GPT, LLMs and DALLE 2)
  • Understand ChatGPT architecture and its capabilities
  • Understanding the difference between language models and chatbots
  • Introduction to OpenAI API and ChatGPT integration

Natural Language Processing Essentials

  • Fundamentals of language models and Natural Language Processing (NLP)
  • Review of fundamental NLP concepts such as tokenization, part-of-speech tagging, and Named Entity Recognition (NER)
  • Understanding sentiment analysis and text classification
  • Text preprocessing techniques for ChatGPT applications such as stemming, lemmatization, and stopword removal

Building a Chatbot with ChatGPT

  • Understanding the design process of a chatbot
  • Choosing the right dataset for training ChatGPT
  • Building a chatbot interface with Python and OpenAI API
  • Fine-tuning a pre-trained ChatGPT model for specific use cases

Content Creation with ChatGPT

  • Overview of content creation with ChatGPT
  • Structuring prompts for content creation tasks (summarization, storytelling, Q&A)
  • Generating text using ChatGPT for various use cases 
  • Preprocessing text data for content creation
  • Implementing ChatGPT into a content creation application using Python

Advanced ChatGPT Techniques

  • Introduction to advanced concepts such as transfer learning and attention mechanisms
  • Fine-tuning ChatGPT for specific use cases
  • Understanding the limitations of ChatGPT and how to overcome them
  • Tips and tricks for optimizing ChatGPT performance and reducing model bias

Project Implementation

  • Applying the concepts learned in the course to a real-world ChatGPT-powered project
  • Working with a team to design and build a ChatGPT application
  • Troubleshooting and optimizing performance, debugging, and refining AI-generated outputs

Best Practices and Ethics in ChatGPT

  • Understanding the ethical concerns and potential biases associated with using ChatGPT
  • Review of best practices for ensuring fair and unbiased results
  • Ethical considerations for the development and deployment of ChatGPT-powered applications
  • Strategies for responsible use of ChatGPT in sensitive or controversial applications

Future Developments in ChatGPT

  • Overview of the latest research and developments in the field of ChatGPT
  • Discussion of emerging use cases for ChatGPT
  • Opportunities for further development and improvement of ChatGPT technology
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

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