Generative AI: OpenAI API, DeepSeek, and ChatGPT in Python from Udemy

Welcome to the forefront of artificial intelligence with our groundbreaking course on Generative AI (GenAI), the OpenAI API, DeepSeek, and ChatGPT. With ChatGPT and DeepSeek, you'll learn how to build with the world's most advanced Large Language Models (LLMs). This course is a must-have if you want to know how to use this cutting-edge technology for your business and work projects.

This course contains 5 main sections:

Basic API Usage: All the fundamentals: signup for an account, get your API key, set environment variables on Windows / Linux / Mac, using the API in Python, setup billing, understand the pricing model, and OpenAI's usage policies. Of note is the chatbot tutorial, which goes over how to incorporate chat history into the model so that ChatGPT "remembers" what it said to you previously. A customer service chatbot will serve as a running example throughout this course.
Prompt Engineering: ChatGPT Prompt Engineering for Developers - All about how to make ChatGPT do what you want it to do. We'll explore various example use-cases, such as getting ChatGPT to output structured data (JSON, tables), sentiment analysis, language translation, creative writing, text summarization, and question-answering. We'll explore techniques like chain-of-thought (CoT) prompting, and we'll even look at how to use ChatGPT to build a stock trading system.
Retrieval Augmented Generation (RAG): Learn how to incorporate external data into LLMs. This powerful technique helps mitigate a common problem called "hallucination". It's critical if you have proprietary data (like product info for your company) that your LLM doesn't know about. You'll learn how semantic search / similarity search works, and how to implement it using FAISS (Facebook AI Similarity Search library). Learn how this will allow you to "chat with your data".
Fine-Tuning: Learn how to "train" an LLM on your own dataset so that it behaves the way you want it to. Sometimes prompt engineering and RAG won't cut it.
GPT-4 with Vision: Everything in this course can be done with GPT-4, but what makes GPT-4 (and GPT-4 Turbo) special is its vision capabilities. That is, it can understand images. In this section, we'll explore many of the amazing applications of combined text-image understanding, some of which include automated homework grading, explaining memes and humor, handwriting transcription, web development, game development, and writing product descriptions based on images (business owners - you already know how this will skyrocket your productivity).

Throughout this course, you'll engage in hands-on exercises, real-world applications, and expert guidance to solidify your understanding and mastery of generative AI concepts. Whether you're a seasoned developer, aspiring AI enthusiast, or industry professional, this course offers a transformative experience that will empower you to harness the true potential of AI.

Are you ready to embark on this exhilarating journey into the future of AI? Join us and unlock the endless possibilities of Generative AI today.

Suggested Prerequisites:

Python coding

What's inside

Learning objectives

How to setup and use the openai api with chatgpt
How to effectively use prompt engineering
Rag (retrieval-augmented generation) with the openai embeddings api

Faiss (facebook ai similarity search)
How to fine-tune chatgpt

How to setup and use the openai api with chatgpt
How to effectively use prompt engineering
Rag (retrieval-augmented generation) with the openai embeddings api
Faiss (facebook ai similarity search)
How to fine-tune chatgpt

Syllabus

Welcome!

Introduction and Outline

Where To Get the Code

How to Succeed in this Course

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers prompt engineering, which is essential for effectively using LLMs like ChatGPT to generate desired outputs for various applications

Explores Retrieval Augmented Generation (RAG), a powerful technique to mitigate hallucination and incorporate external data into LLMs, which is critical for proprietary data

Teaches how to fine-tune LLMs on custom datasets, which is useful when prompt engineering and RAG are insufficient to achieve desired model behavior

Requires Python coding experience, which may be a barrier for individuals without a programming background

Uses FAISS (Facebook AI Similarity Search), which is a specific library, so learners should be aware of its potential limitations and alternatives

Focuses on the OpenAI API, DeepSeek, and ChatGPT, so learners should be aware that the knowledge may not be directly transferable to other LLMs or platforms

Reviews summary

Practical genai with openai and deepseek

According to learners, this course offers a highly practical approach to generative AI using the OpenAI and DeepSeek APIs. Students particularly praise the hands-on coding examples and the deep dive into effective prompt engineering techniques, which help them control LLM behavior for specific tasks. The section on Retrieval Augmented Generation (RAG) is highlighted as particularly useful for integrating external data and mitigating hallucinations. Reviewers appreciate that the course content is kept up-to-date, including recent models like GPT-4o mini. While the course provides strong value, some learners note that it moves at a fast pace and requires a solid Python background to follow comfortably. Overall, it's considered a valuable resource for applying GenAI in real-world projects.

Provides guidance on setting up the coding environment.

"The appendix on environment setup was helpful, though setup can still be tricky."

"I still struggled a bit getting everything installed correctly, but the notes helped."

"Wish there were more detailed debugging tips for common code or environment issues."

"Getting the APIs and libraries set up took some effort despite the instructions."

Course is updated with recent LLM advancements.

"Appreciate the updates on newer models like GPT-4o mini; keeps the course relevant."

"Good to see DeepSeek included alongside OpenAI; shows commitment to current tech."

"The instructor clearly keeps the course material current with the fast-changing AI field."

"It's great that they add new content as the technology evolves."

Offers practical methods for incorporating external data.

"The RAG section clearly explained how to 'chat with my data' using embeddings."

"Using FAISS with the OpenAI embeddings API made perfect sense after this module."

"Helped me understand how to mitigate hallucination issues with proprietary data."

"This technique is critical for business applications and was well covered."

Teaches effective techniques for controlling LLM output.

"The section on prompt engineering unlocked so many possibilities for me."

"Learning chain-of-thought prompting was incredibly useful for complex tasks."

"Now I can get the model to output structured data like JSON reliably."

"This section is a must-have for anyone working with LLMs."

Focuses on hands-on coding with APIs.

"Learned how to integrate the OpenAI API into my projects quickly."

"The code examples for connecting to DeepSeek were very clear and helpful."

"Gave me the confidence to start building API-driven applications."

"I found the practical examples for API usage immediately applicable."

Assumes a strong Python and basic AI understanding.

"Found the pace a bit fast in some sections; definitely need a solid grasp of Python."

"Some concepts felt advanced without prior exposure to machine learning or NLP basics."

"This is definitely for developers with existing Python skills, not absolute beginners."

"Make sure your Python foundation is strong before taking this course."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI: OpenAI API, DeepSeek, and ChatGPT in Python with these activities:

Review Python Fundamentals

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Strengthen your Python foundation to better understand the code examples and implement your own GenAI projects.

Browse courses on Python Basics

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Review basic data types (strings, integers, lists, dictionaries).
Practice writing functions and using control flow (if/else, loops).
Familiarize yourself with common Python libraries (e.g., NumPy, Pandas).

Read 'Natural Language Processing with Python'

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Gain a deeper understanding of NLP fundamentals to better grasp the concepts behind LLMs and prompt engineering.

View Natural Language Processing with Python:... on Amazon

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Read the chapters on text processing and feature extraction.
Experiment with the NLTK library to perform basic NLP tasks.
Relate the concepts learned to the prompt engineering techniques covered in the course.

Experiment with Prompt Engineering

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Improve your prompt engineering skills by experimenting with different prompts and analyzing the results.

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Choose a specific task (e.g., summarization, translation, question answering).
Create a set of prompts with varying levels of detail and complexity.
Evaluate the output of ChatGPT for each prompt and identify patterns.
Refine your prompts based on the evaluation results.

Four other activities

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Build a Customer Service Chatbot

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Apply your knowledge to build a practical application that utilizes the OpenAI API and prompt engineering techniques.

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Define the scope and functionality of the chatbot.
Design the chatbot's conversation flow and prompts.
Implement the chatbot using Python and the OpenAI API.
Test and refine the chatbot's performance.

Write a Blog Post on RAG

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Solidify your understanding of RAG by explaining the concept and its implementation in a blog post.

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Research and gather information on RAG and its applications.
Outline the structure of your blog post.
Write a clear and concise explanation of RAG.
Include examples and code snippets to illustrate the concepts.
Edit and proofread your blog post before publishing.

Read 'Generative Deep Learning'

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Expand your knowledge of generative models beyond LLMs to gain a broader understanding of the field.

View Generative Deep Learning 2nd Edition... on Amazon

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Read the chapters on different types of generative models.
Compare and contrast the different models.
Relate the concepts learned to the LLMs covered in the course.

Contribute to a FAISS Project

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Deepen your understanding of FAISS by contributing to an open-source project that utilizes it.

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Find an open-source project that uses FAISS.
Identify a bug or feature that you can contribute to.
Implement the fix or feature and submit a pull request.
Respond to feedback from the project maintainers.

Career center

Learners who complete Generative AI: OpenAI API, DeepSeek, and ChatGPT in Python will develop knowledge and skills that may be useful to these careers:

AI Prompt Engineer

The AI Prompt Engineer is responsible for crafting effective prompts that elicit desired responses from large language models. As an AI Prompt Engineer, this course helps you understand how to make ChatGPT do what you want it to do. You'll explore various example use-cases, such as getting ChatGPT to output structured data, perform sentiment analysis, translate languages, write creatively, summarize text, and answer questions. This course explores techniques like chain-of-thought prompting and using ChatGPT to build systems. If you're keen on shaping the interaction between humans and AI, this course provides invaluable skills.

See salaries and explore the career path for AI Prompt Engineer

Generative AI Specialist

A Generative AI Specialist focuses on creating new content using AI models. This course will be immediately applicable to the Generative AI Specialist, delving into the OpenAI API, DeepSeek, and ChatGPT. The course provides hands-on exercises and real-world applications to solidify your understanding and mastery of generative AI concepts. The section on GPT-4 with vision, which explores applications of combined text-image understanding, is perfect for Generative AI Specialists interested in creating innovative content.

See salaries and explore the career path for Generative AI Specialist

AI Application Developer

An AI Application Developer builds applications that leverage the power of artificial intelligence. This course will be useful for the AI Application Developer, since it provides a hands-on approach to using the OpenAI API, DeepSeek, and ChatGPT in Python. The course covers fundamental API usage, prompt engineering, retrieval augmented generation, fine-tuning, and GPT-4 with vision. You will learn how to incorporate external data into LLMs to mitigate hallucination and how to train an LLM on your own dataset to customize its behavior. If you want to develop AI-powered applications, this course is an excellent starting point.

See salaries and explore the career path for AI Application Developer

Chatbot Developer

The Chatbot Developer designs and implements conversational AI systems. The course is a resource for a Chatbot Developer, since it teaches you how to incorporate chat history into the model so that ChatGPT remembers what it said previously. A customer service chatbot serves as a running example throughout this course. Given the importance of user experience in chatbot design, the prompt engineering section of the course, which covers techniques for eliciting specific responses from ChatGPT, is particularly relevant for Chatbot Developers looking to fine-tune their chatbot's conversational abilities.

See salaries and explore the career path for Chatbot Developer

AI Solutions Architect

The AI Solutions Architect designs and implements AI solutions for business problems. This course helps the AI Solutions Architect understand how to incorporate external data into LLMs. The retrieval augmented generation section teaches you how semantic search or similarity search works, and how to implement it using FAISS. If you are passionate about using AI to solve complex problems, this course provides the practical skills needed to build effective AI solutions.

See salaries and explore the career path for AI Solutions Architect

Natural Language Processing Engineer

The Natural Language Processing Engineer focuses on developing algorithms that enable computers to understand and process human language. This course can be useful to the Natural Language Processing Engineer, since it covers the fundamentals of using the OpenAI API with ChatGPT. The course helps the Natural Language Processing Engineer dive into prompt engineering and retrieval augmented generation, both of which are critical for building NLP applications that require contextual understanding and accurate responses. The discussion of temperature, frequency, and presence penalties are also critical.

See salaries and explore the career path for Natural Language Processing Engineer

Computer Vision Engineer

The Computer Vision Engineer designs and implements systems that enable computers to see and interpret images. This course provides knowledge for the Computer Vision Engineer, since it covers GPT-4 with vision. This section explores applications of combined text-image understanding, such as automated homework grading, explaining memes and humor, handwriting transcription, web development, game development, and writing product descriptions based on images. If you are interested in building systems that combine text and image understanding, this course provides valuable knowledge.

See salaries and explore the career path for Computer Vision Engineer

AI Research Scientist

An AI Research Scientist researches and develops new AI algorithms and techniques. For the AI Research Scientist, this course provides a solid understanding of the current state-of-the-art in generative AI, including the OpenAI API, DeepSeek, and ChatGPT. The course's coverage of retrieval augmented generation and fine-tuning techniques helps researchers explore new ways to improve the performance and capabilities of LLMs. This can provide a practical understanding of existing tools while pushing the boundaries of AI research.

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Technical Consultant

The Technical Consultant provides expert advice and guidance to clients on technology-related issues. This course may be useful to Technical Consultants, as it provides a broad understanding of generative AI technologies. The course will equip you with the knowledge to advise clients on how to leverage AI to improve their business processes and develop innovative solutions. Understanding API usage, prompt engineering, and fine-tuning techniques are all relevant.

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Data Scientist

The Data Scientist analyzes data to extract meaningful insights and build predictive models. This course will be useful to Data Scientists eager to leverage the power of generative AI in their analyses. You'll learn how to fine-tune ChatGPT on your own dataset, allowing you to tailor the model's behavior to specific analytical tasks. The knowledge of prompt engineering and retrieval augmented generation helps Data Scientists leverage LLMs to extract insights and build more accurate models.

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AI Product Manager

The AI Product Manager is responsible for defining and managing the development of AI-powered products. This course may be useful for the AI Product Manager. By diving into the OpenAI API, DeepSeek, and ChatGPT, you will be better equipped to guide product development and make informed decisions about AI integration. The course’s coverage of real-world applications helps Product Managers align AI initiatives with business goals.

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Software Engineer

The Software Engineer designs, develops, and tests software applications. The course may be useful for the Software Engineer who wants to incorporate AI into their projects. It will equip you with the skills to leverage cutting-edge AI technologies like ChatGPT and DeepSeek. The course's hands-on exercises and real-world applications help you understand how to integrate AI into existing software systems.

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Machine Learning Engineer

The Machine Learning Engineer develops and deploys machine learning models. This course may be useful to Machine Learning Engineers, since it will provide an understanding of the OpenAI API, DeepSeek, and ChatGPT. You'll learn how to fine-tune ChatGPT on your own dataset so that it behaves the way you want it to. The course also covers how to incorporate external data into LLMs and how to implement semantic search using FAISS.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

The Data Analyst collects, processes, and analyzes data to identify trends and insights. This course may be useful for Data Analysts interested in leveraging AI to enhance their analytical capabilities. You will learn how to use ChatGPT for sentiment analysis, summarization, and question-answering. Incorporating AI into your analytical workflows can significantly improve the efficiency and accuracy of data analysis tasks.

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AI Ethicist

The AI Ethicist is responsible for ensuring the ethical development and deployment of AI technologies. This course may be useful for AI Ethicists. By delving into the capabilities and limitations of models like ChatGPT, you will gain a better understanding of potential ethical implications. Understanding prompt engineering and usage policies can help the AI Ethicist identify and address biases, promote fairness, and ensure responsible AI practices.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI: OpenAI API, DeepSeek, and ChatGPT in Python.

Generative Deep Learning

Save

Provides a comprehensive overview of generative models, including GANs, VAEs, and autoregressive models. While the course focuses on LLMs, understanding other generative models can provide a broader perspective on the field. This book is particularly helpful for understanding the underlying principles of generative AI. It serves as a valuable reference for those interested in the theoretical foundations of the field.

(中文) Generative Deep Learning 2nd Edition...

Paperback

$$$$

Natural Language Processing with Python

Save

Provides a comprehensive introduction to NLP concepts and techniques using Python and the NLTK library. While the course focuses on GenAI and LLMs, understanding the fundamentals of NLP will provide a solid foundation. This book is particularly helpful for understanding text processing, tokenization, and other essential NLP tasks. It serves as a valuable reference for those new to the field.

Natural Language Processing with Python: Analyzing...

Paperback

$$$

Natural Language Processing with Python: Analyzing...

Kindle Edition

Generative AI

OpenAI API, DeepSeek, and ChatGPT in Python

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical genai with openai and deepseek

Activities

Career center

Reading list

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