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Getting started with ChatGPT

Give a booster shot to team productivity by adding ChatGPT capabilities to your apps.


5 Days


Basic Level

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As per your team needs

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ChatGPT is a powerful language model developed by OpenAI that can generate human-like responses to natural language inputs. It is capable of understanding and generating text in a variety of languages and can be used for a wide range of applications, such as chatbots, customer service, and content creation. This outline is intended for Python developers who want to learn how to use ChatGPT in their projects.

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The target audience for this course is Python developers who have a basic understanding of programming concepts and want to learn how to incorporate natural language processing into their projects. This course is also suitable for individuals who have experience with other programming languages and want to learn Python for NLP.

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  • Overview of Generative AI (GPT, LLMs and DALE2)
  • Understand ChatGPT and its capabilities
  • Understanding the difference between language models and chatbots
  • Introduction to OpenAI API and how to access ChatGPT
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Recap of key concepts and skills learned in the course
  • Suggestions for further learning and development of ChatGPT skills
  • Guidance for continuing to improve and apply ChatGPT knowledge in future projects
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  • 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.


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