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Using ChatGPT in Python for NLP Applications

Course Summary

This course introduces participants to the powerful language model, ChatGPT, that was developed by OpenAI. Able to generate human-like responses to natural language inputs, ChatGPT can be used in a wide range of applications, such as chatbots, customer service, and content creation. Hands-on activities throughout the course ensure that participants gain practical experience in using ChatGPT within Natural Language Processing (NLP) applications in a variety of real-world scenarios.

Purpose
Recognize the power of ChatGPT and how it can generate human-like responses to natural language inputs.
Audience

This course was designed for:

Python developers who have a basic understanding of programming concepts and want to learn how to incorporate natural language processing into their projects.

Individuals who have experience with other programming languages and want to learn Python for NLP.

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

 

Prerequisites
  • A basic understanding of Python programming
  • Familiarity with natural language processing concepts such as tokenization, part-of-speech tagging, and sentiment analysis
  • A working knowledge of machine learning concepts such as supervised and unsupervised learning
  • Some understanding of Language Models

What you'll learn:

In this course, you'll learn:
  • Introduction to ChatGPT
    • Overview of Generative AI ( GPT, LLMs and DALLE 2)
    • Understand ChatGPT and its capabilities
    • Understanding the difference between language models and chatbots
    • Introduction to OpenAI API and how to access ChatGPT
  • Natural Language Processing Essentials
    • Language Model Fundamentals.
    • Review of fundamental NLP concepts such as tokenization, part-of-speech tagging, and named entity recognition
    • Understanding sentiment analysis and text classification
    • Text preprocessing techniques such as stemming and lemmatization
  • Building a Chatbot with ChatGPT
    • Understanding the design process of a chatbot
    • Choosing the right dataset for training ChatGPT
    • Preprocessing text data for use in ChatGPT
    • Fine-tuning a pre-trained ChatGPT model for specific use cases
    • Implementing ChatGPT into a chatbot using Python
  • Content Creation with ChatGPT
    • Overview of content creation with ChatGPT
    • Understanding the use of prompts and completions
    • Generating text using ChatGPT for various use cases such as article writing and summarization
    • 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 project
    • Working with a team to design and build a ChatGPT application
    • Troubleshooting and optimizing the application for maximum performance
  • 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

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