Secure ChatGPT

Course description:

In this two-day course, participants will delve into the world of large language models, with a specific focus on ChatGPT. The course covers a wide range of topics, from understanding the fundamental concepts and history of ChatGPT and GPT model family to exploring their applications and limitations, including ChatGPT alternatives.

Participants will learn how to use ChatGPT securely and responsibly in various industries and use cases from translation to software engineering, content creation, sentiment analysis and text summarization. They will understand how prompt engineering works and also work with various APIs for popular tasks.

Prerequisites:

No prerequisites needed for this course.

Who is it for:

This Secure ChatGPT course is designed for:

  • Entrepreneurs and product managers who want to explore business opportunities and innovations related to NLP in various industries.
  • AI and Machine Learning enthusiasts interested in understanding large language models, specifically ChatGPT, and their practical applications.
  • Data Scientists and Data Analysts looking to incorporate ChatGPT in their projects and understand the potential risks and limitations.
    Software Developers seeking to integrate ChatGPT and its functionalities into their applications securely and responsibly.
  • Business Analysts and Decision Makers who want to comprehend the potential impact of ChatGPT on their industry and explore its use cases.
  • Information Security Professionals responsible for ensuring data privacy and secure usage of AI technologies within their organizations.

What will you learn:

  • Understand the main concepts of Large Language Models and ChatGPT, including their capabilities and applications.
  • Compare ChatGPT with other models in the GPT family, analyzing their history, providers, licensing models, and data protection aspects.
    Explore the impact of using ChatGPT in different industries, identifying potential benefits and challenges in each sector.
  • Identify various use cases of ChatGPT, such as translation, code generation and debugging, content creation, summarization, and sentiment analysis.
  • Gain proficiency in prompting and prompt engineering techniques to effectively interact with ChatGPT and elicit desired responses.
  • Explore different approaches to prompt engineering, including Standard, Role, Zero-shot, Few-shot, and Chain of Thoughts, and understand their strengths and weaknesses.
  • Analyze the risks and limitations associated with using ChatGPT, with a focus on data privacy and security aspects, and learn how to mitigate potential risks.
  • Design a potential architecture for secure enterprise usage of ChatGPT, considering privacy and security concerns.
  • Compare the OpenAI and Azure Cognitive Services APIs, evaluate their pros and cons, and review existing models available through these APIs.
  • Implement Python examples for popular tasks using the OpenAI API, gaining practical experience in working with ChatGPT programmatically.
  • Create a Python app that leverages the ChatGPT API to convert meeting notes into summaries, applying the knowledge acquired during the course.
  • Learn how to implement anonymization and masking techniques in Python to protect sensitive information when using ChatGPT.
  • Develop an anonymization module within a ChatGPT connection, ensuring secure handling of data during interactions.
  • Explore alternative approaches to using proprietary ChatGPT technology, understanding the considerations and implications of such choices.

1. Introduction to main concepts of Large Language Models and ChatGPT.
2. ChatGPT and GPT model family comparison: history, providers, licensing model, and data protection aspects.
3. Impact of using ChatGPT in different industries.
4. Use cases with ChatGPT: translation, code generation and debugging, content creation, summarization, sentiment analysis.
5. Prompts and prompt engineering in ChatGPT.
6. Approaches around prompt engineering: Standard, Role, Zero-shot, Few-shot, Chain of Thoughts.
7. Risks and limitations in using ChatGPT (including data privacy and security aspects).
8. Use case: A potential architecture for enterprise secure usage of ChatGPT.
9. Using the OpenAI and Azure Cognitive Services APIs: pros and cons, review of existing models.
10. Python examples of doing popular tasks using OpenAI API.
11. Exercise: Implementing a python app that converts meeting notes to summary using ChatGPT API.
12. How to implement anonymization and masking in Python.
13. Exercise: Implement an anonymization module in a ChatGPT connection.
14. Alternatives in using proprietary ChatGPT technology.

Certification:

Certificate of completion.

Secure ChatGPT​

€ 840
  • DURATION: 2 days
  • SKILL LEVEL: Associate
  • LECTURES: 14 lessons

Register
  • PRICE: 840 €
  • DURATION: 2 days
  • SKILL LEVEL: Associate
  • LECTURES: 14 lessons

Course description:

In this two-day course, participants will delve into the world of large language models, with a specific focus on ChatGPT. The course covers a wide range of topics, from understanding the fundamental concepts and history of ChatGPT and GPT model family to exploring their applications and limitations, including ChatGPT alternatives.

Participants will learn how to use ChatGPT securely and responsibly in various industries and use cases from translation to software engineering, content creation, sentiment analysis and text summarization. They will understand how prompt engineering works and also work with various APIs for popular tasks.

Prerequisites:

No prerequisites needed for this course.

Who is it for:

This Secure ChatGPT course is designed for:

  • Entrepreneurs and product managers who want to explore business opportunities and innovations related to NLP in various industries.
  • AI and Machine Learning enthusiasts interested in understanding large language models, specifically ChatGPT, and their practical applications.
  • Data Scientists and Data Analysts looking to incorporate ChatGPT in their projects and understand the potential risks and limitations.
    Software Developers seeking to integrate ChatGPT and its functionalities into their applications securely and responsibly.
  • Business Analysts and Decision Makers who want to comprehend the potential impact of ChatGPT on their industry and explore its use cases.
  • Information Security Professionals responsible for ensuring data privacy and secure usage of AI technologies within their organizations.

What will you learn:

  • Understand the main concepts of Large Language Models and ChatGPT, including their capabilities and applications.
  • Compare ChatGPT with other models in the GPT family, analyzing their history, providers, licensing models, and data protection aspects.
    Explore the impact of using ChatGPT in different industries, identifying potential benefits and challenges in each sector.
  • Identify various use cases of ChatGPT, such as translation, code generation and debugging, content creation, summarization, and sentiment analysis.
  • Gain proficiency in prompting and prompt engineering techniques to effectively interact with ChatGPT and elicit desired responses.
  • Explore different approaches to prompt engineering, including Standard, Role, Zero-shot, Few-shot, and Chain of Thoughts, and understand their strengths and weaknesses.
  • Analyze the risks and limitations associated with using ChatGPT, with a focus on data privacy and security aspects, and learn how to mitigate potential risks.
  • Design a potential architecture for secure enterprise usage of ChatGPT, considering privacy and security concerns.
  • Compare the OpenAI and Azure Cognitive Services APIs, evaluate their pros and cons, and review existing models available through these APIs.
  • Implement Python examples for popular tasks using the OpenAI API, gaining practical experience in working with ChatGPT programmatically.
  • Create a Python app that leverages the ChatGPT API to convert meeting notes into summaries, applying the knowledge acquired during the course.
  • Learn how to implement anonymization and masking techniques in Python to protect sensitive information when using ChatGPT.
  • Develop an anonymization module within a ChatGPT connection, ensuring secure handling of data during interactions.
  • Explore alternative approaches to using proprietary ChatGPT technology, understanding the considerations and implications of such choices.

1. Introduction to main concepts of Large Language Models and ChatGPT.
2. ChatGPT and GPT model family comparison: history, providers, licensing model, and data protection aspects.
3. Impact of using ChatGPT in different industries.
4. Use cases with ChatGPT: translation, code generation and debugging, content creation, summarization, sentiment analysis.
5. Prompts and prompt engineering in ChatGPT.
6. Approaches around prompt engineering: Standard, Role, Zero-shot, Few-shot, Chain of Thoughts.
7. Risks and limitations in using ChatGPT (including data privacy and security aspects).
8. Use case: A potential architecture for enterprise secure usage of ChatGPT.
9. Using the OpenAI and Azure Cognitive Services APIs: pros and cons, review of existing models.
10. Python examples of doing popular tasks using OpenAI API.
11. Exercise: Implementing a python app that converts meeting notes to summary using ChatGPT API.
12. How to implement anonymization and masking in Python.
13. Exercise: Implement an anonymization module in a ChatGPT connection.
14. Alternatives in using proprietary ChatGPT technology.

Certification:

Certificate of completion.

Register

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