5 Free ChatGPT and Generative AI Courses

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In technology, proficiency in using the latest generation of generative artificial intelligence (AI) tools is rapidly becoming important. These tools like ChatGPT and Bard have proven to be fun and have features that really help in different areas of life. To help you embrace this new era of generative AI, we’ve put together a top-notch course that will serve as an invaluable resource to hone your skills and stay at the forefront of this transformative technology.

By exploring these materials and making the most of these groundbreaking capabilities, you can improve your knowledge and ability to fully harness the power of generative AI.

ChatGPT prompt engineering for developers

In the ChatGPT Prompt Engineering for Developers course taught by Andrew Ng and Isa Fulford, learn how to use Large Language Models (LLM) to quickly build effective apps. Users can now create features that were too expensive, too complex, or perhaps impossible using the OpenAI API.

The course includes prompt engineering best practices, insight into how LLM works, and examples of how to use the LLM API for various tasks. Summarizing user ratings, determining sentiment, identifying subject lines, translating or correcting the grammar of text, and augmenting material by auto-generating emails are some of these tasks.

This course covers systematic prompt engineering, focusing on two key principles for creating powerful prompts. There is also an opportunity to create a unique chatbot. Leverage case studies and a hands-on Jupyter Notebook environment to learn skills for timely engineering.

Delivered in partnership with OpenAI, this training aims to equip developers with the knowledge and competencies they need to effectively use LLM. This course is suitable for you, regardless of your level of Python proficiency or interest in cutting-edge prompt engineering and the use of LLM.

LangChain for LLM application development

Enroll in the “LangChain for LLM Application Development” course to learn key competencies for enhancing language model functionality in application development utilizing the LangChain framework. In this course, the user will learn how to call the LLM, create prompts, parse responses, use memory for conversation, create sequences of operations, implement question-answering for documents, and the evolution of his LLM as an inference agent. Learn how to explore.

By the end of the course, participants will have a model that they can use as a starting point for further research and application development of the dissemination model. Taught by Andrew Ng and LangChain co-founder Harrison Chase, this one-hour workshop will help participants build reliable applications quickly. This course is suitable for beginners. However, some knowledge of Python is helpful.

Related: 5 Free Artificial Intelligence Courses and Certifications

How diffusion models work

Participants who want to create a diffusion model from scratch should take the “How Diffusion Models Work” course. This intermediate-level course provides a thorough understanding of the models used in the diffusion process. Participants learn to build their own diffusion models and acquire useful coding skills.

During the course, participants will:

  • Develop your own diffusion models as you explore the field of diffusion-based generative AI.
  • Go beyond pre-built solutions and APIs to gain a thorough understanding of the diffusion process and underlying models.
  • Gain hands-on coding skills through labs on sampling, training diffusion models, creating neural networks for noise prediction, and incorporating context for personalized image generation.
  • You will end the course with a model that serves as a starting point for further exploration of diffusion models in your own applications.

Led by Sharon Zhou, this one-hour session focuses on creating, refining, and optimizing diffusion models to enhance participants’ generative AI capabilities. With hands-on examples and her built-in Jupyter Notebook, participants can easily understand and extend the concepts provided.

Building a system using the ChatGPT API

The Building Systems with ChatGPT API course teaches participants how to automate complex workflows by making a series of calls to powerful language models. This concise course will improve your development skills and increase your productivity. Individuals will:

  • Create a series of prompts that respond to previous completions.
  • Create technology that allows Python programs to communicate with new prompts and completions.
  • Apply the principles taught in the course to create a chatbot for customer support.
  • Use these capabilities in real-world situations, such as user query classification, safety evaluation, and multi-step reasoning.

Taught by Ng of DeepLearning.AI and Fulford of OpenAI, this 1-hour session expands on “ChatGPT Prompt Engineering for Developers” (not a prerequisite). Jupyter Notebooks and hands-on examples make it easy to understand and explore course materials.

Collaboration within the OpenAI community ensures current best practices for optimal performance and responsible use. This course is suitable not only for those with a basic knowledge of Python, but also for intermediate and advanced ML engineers looking for cutting-edge, rapid engineering skills in language models.

Related: 5 Real-World Applications of Natural Language Processing (NLP)

Overview of ChatGPT

Join DataCamp’s “Getting Started with ChatGPT” course to gain the knowledge you need to use ChatGPT effectively and responsibly. This course covers ChatGPT features and limitations and is suitable for users of all skill levels. Find ChatGPT suggestions for new integration opportunities, business use cases, and best practices.

The course is divided into two modules: ‘Interacting with ChatGPT’ which is available free of charge and ‘Introduction to ChatGPT’ which is available through purchase or DataCamp subscription. By the end of the course, participants will be able to confidently apply his ChatGPT in a variety of situations, improving their speed and efficiency on a wide range of tasks.



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