Unleash the Power of AI: Master OpenAI's APIs, including GPT-4, DALL-E, and Whisper in this Comprehensive and Hands-On Course. This is a brand new course, recorded with GPT-4! Step into the world of artificial intelligence and discover how to harness OpenAI's cutting-edge APIs, including GPT3, GPT-3.5, GPT4, DALL-E, and Whisper, to create groundbreaking applications and solutions. This immersive, project-driven course is designed for learners of all backgrounds and skill levels, providing a solid foundation in AI-driven development.
Mastering OpenAI Python APIs: Unleash ChatGPT and GPT4
13h 4m 58s
English
Free
In this comprehensive course, you will:
- Develop a deep understanding of OpenAI's generative models and their potential applications
- Master GPT-4 for natural language processing, including text generation, summarization, translation, and more
- Use GPT-4 to debug code, improve code, and even write code from scratch
- Generate visually stunning images and artwork using DALL-E based on textual prompts
- Convert spoken audio into accurate transcriptions and translations with the power of Whisper
- Use AI Embeddings to distill, index, search, and compare text, unveiling the geometric power for comparing words, paragraphs, and documents
Real-World Projects for Practical Experience:
- Create a dynamic Q&A Bot using GPT-4
- Create a visual color palette search engine with GPT-4
- Write an interactive code reviewing assistant with GPT-4
- Create an AI-powered Spotify playlist generator
- Analyze the sentiment of Reddit comments using GPT-4
- Summarize books of any size into a couple of paragraphs
- Create your own interactive, infinite Choose Your Own Adventure application with DALL-E and GPT-4
- Generate an Emedding-Powered movie recommendation algorithm
Throughout the course, you will engage in hands-on projects and real-world examples, allowing you to immediately apply your newly-acquired knowledge and skills. We also delve into:
- Best practices for prompt engineering, tokenization, and temperature settings
- Strategies for optimizing API performance, error handling, and resource management
- The ethical considerations and challenges associated with AI-driven development
Whether you're a software developer, data scientist, or an AI enthusiast, this course will equip you with the expertise to integrate OpenAI's APIs into your projects and create innovative AI-powered solutions.
Embark on your AI journey and transform your skills with this comprehensive and engaging course. Enroll today and start unlocking the limitless potential of OpenAI's GPT-4, DALL-E, and Whisper APIs!
About the Author: Udemy
Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.
Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.
Watch Online 111 lessons
- Space or K: play or pause
- J: rewind 10 seconds
- L: forward 10 seconds
- Left Arrow: rewind 5 seconds
- Right Arrow: forward 5 seconds
- Up Arrow: volume up
- Down Arrow: volume down
- M: mute or unmute
- F: toggle fullscreen
- T: toggle theater mode
- I: toggle mini player
- 0 to 9: seek to 0 to 90 percent of the video
- Shift plus N: next video
- Shift plus P: previous video
0:00 0:00
| # | Lesson Title | Duration |
|---|---|---|
| 1 | Welcome & Course Overview | 11:03 |
| 2 | A Brief History of OpenAI | 05:58 |
| 3 | Let's Talk About GPT | 06:33 |
| 4 | OPTIONAL: The Transformer Architecture | 11:25 |
| 5 | Signing Up For An Account | 03:48 |
| 6 | Getting The Course Code | 00:40 |
| 7 | Our First Completion Request | 08:07 |
| 8 | Hiding Our API Key | 06:08 |
| 9 | Understanding Tokens | 03:08 |
| 10 | max_tokens | 06:21 |
| 11 | Stop Sequences | 10:09 |
| 12 | N and Echo | 09:27 |
| 13 | The Different Completion Models | 10:10 |
| 14 | Comparing Model Performance and Pricing | 05:37 |
| 15 | The Elements of a Good Prompt | 10:36 |
| 16 | Controlling The Output Format | 07:34 |
| 17 | Summarization Prompts | 03:34 |
| 18 | Data Extraction Prompts | 03:06 |
| 19 | Sentiment Analysis Prompts | 03:27 |
| 20 | Zero-Shot Vs. Few-Shot Prompting | 04:47 |
| 21 | "Let's Think Step By Step" Prompting | 03:31 |
| 22 | Text Transformation Prompts | 05:04 |
| 23 | Introducing The Color Palette Project | 03:57 |
| 24 | Writing The Color Palette Generator Prompt | 08:44 |
| 25 | Writing a Color-Swatch Rendering Function | 11:31 |
| 26 | Setting Up The Flask Server | 06:03 |
| 27 | Integrating OpenAI With Server | 08:14 |
| 28 | Writing the Palette Endpoint | 08:43 |
| 29 | Creating The Form | 09:46 |
| 30 | Rending The Colors In The Browser | 06:24 |
| 31 | Copy and Paste Functionality | 06:21 |
| 32 | Styling The Color Blocks | 06:42 |
| 33 | Styling The Form | 07:14 |
| 34 | Refactoring Our Front-End Code | 07:04 |
| 35 | Temperature | 11:51 |
| 36 | Understanding Top P | 09:07 |
| 37 | Frequency Penalty | 10:03 |
| 38 | Presence Penalty | 04:54 |
| 39 | Streaming Responses | 06:35 |
| 40 | Introducing The Chat API | 03:37 |
| 41 | Our First Chat Request | 05:56 |
| 42 | Important Note On Pricing! | 02:13 |
| 43 | Prompting With Properly Formatted Messages | 08:18 |
| 44 | Note on GPT-3.5-Turbo Versions | 01:45 |
| 45 | Rewriting a Completion Prompt In Chat Format | 08:26 |
| 46 | Chat API Parameters | 05:07 |
| 47 | Introducing Our Chatbot Project | 02:29 |
| 48 | Writing The Basic Chatbot Structure | 07:53 |
| 49 | Persisting Messages Across Requests | 08:46 |
| 50 | Adding Optional Personalities | 09:21 |
| 51 | Colorizing The Chatbot Output | 05:29 |
| 52 | Asking GPT-4 To Explain Code | 06:08 |
| 53 | Calculating Time Complexity With GPT-4 | 07:10 |
| 54 | Translating JS To Python With GPT-4 | 06:12 |
| 55 | Fixing Code Bugs With GPT-4 | 06:30 |
| 56 | Generating Code From Scratch With GPT-4 | 06:17 |
| 57 | Counting Tokens With TikToken | 09:10 |
| 58 | Counting GPT-4 Message Tokens | 07:10 |
| 59 | Introducing The Basic Code Reviewer | 04:08 |
| 60 | Building The Basic Code Reviewer Pt 1 | 08:26 |
| 61 | Building The Basic Code Reviewer Pt 2 | 10:21 |
| 62 | Introducing The Interactive Code Reviewer | 07:45 |
| 63 | Interactive Code Reviewer Prompt | 13:00 |
| 64 | Interactive Code Reviewer Walkthrough | 12:04 |
| 65 | Introducing The Spotify Project | 04:40 |
| 66 | Writing The Playlist Generating Prompt | 12:11 |
| 67 | Finishing The Playlist Prompt | 05:38 |
| 68 | Getting Spotify Developer Credentials | 03:39 |
| 69 | Spotify Authentication Via Python | 07:31 |
| 70 | Searching For Spotify Tracks Via Python | 05:33 |
| 71 | Creating Spotify Playlists Programmatically | 04:52 |
| 72 | Adding in OpenAI | 06:46 |
| 73 | Accepting Command Line Arguments | 06:26 |
| 74 | Overview of the Polished Solution | 05:21 |
| 75 | Introducing Embeddings | 02:39 |
| 76 | Generating a Single Embedding | 03:50 |
| 77 | Introducing The Movie Embedding Visualization | 04:20 |
| 78 | Getting Our Movie Data Ready | 04:39 |
| 79 | Generating Embeddings for 5000 Movies | 12:08 |
| 80 | Visualizing Our Embeddings With Atlas | 09:34 |
| 81 | Recommending Movies Using Our Embeddings | 16:12 |
| 82 | Expanding GPT-4 "Knowledge" With Embeddings | 08:03 |
| 83 | Gathering Our Embeddings | 11:36 |
| 84 | Implementing Q&A With Embeddings & GPT-4 | 09:08 |
| 85 | Introducing The Reddit Sentiment Analysis Project | 04:59 |
| 86 | Collecting Comments From Reddit | 10:10 |
| 87 | Analyzing Sentiment In The Comments | 11:49 |
| 88 | Plotting The Results | 05:57 |
| 89 | Introducing The Book Summarizer | 07:24 |
| 90 | Preparing Book Text For Summarization | 07:16 |
| 91 | Context Window Math | 08:35 |
| 92 | Summarization Logic | 13:44 |
| 93 | Caching Results | 04:55 |
| 94 | Performing a "Meta-Summary" With GPT-4 | 07:56 |
| 95 | Intro To DALL-E | 03:46 |
| 96 | Making a DALL-E Request | 08:04 |
| 97 | Saving DALL-E Images | 12:50 |
| 98 | Alternative Approach To Saving Images | 07:38 |
| 99 | Requesting Image Variations | 08:24 |
| 100 | DALL-E Image Edits | 05:09 |
| 101 | Introducing Stability.AI and Stable Diffusion | 04:56 |
| 102 | Using the Stability SDK | 07:57 |
| 103 | Introducing The Choose Your Own Adventure Project | 05:34 |
| 104 | GPT-4 CYOA Text Generation | 09:58 |
| 105 | Stable Diffusion CYOA Image Generation | 09:06 |
| 106 | Introducing Whisper | 01:58 |
| 107 | The Basics of Whisper | 06:30 |
| 108 | Providing a Prompt to Whisper | 02:48 |
| 109 | Translating Audio With Whisper | 04:52 |
| 110 | Transcribing Non-English Audio | 05:01 |
| 111 | Running The Whisper Model Locally | 05:49 |
Related courses
-
Updated 2y agoThe Fundamentals of Programming with Python
By: Tim Ruscica (Tech With Tim)Learn the Python programming language from scratch. This series is designed for complete beginners and will walk you through the python programming language. Ab4h 18m -
FreeClassic100 Days of Code - The Complete Python Pro Bootcamp for 2023
By: Udemy100 Days of Code: The Complete Python Pro Bootcamp 2023 by Angela Yu — Python basics, web scraping, data science, GUI projects, and more.58h 35m5/5 -
Updated 2y agoModern APIs with FastAPI and Python Course
By: Talk Python TrainingFastAPI is one of the most exciting new web frameworks out today. It's exciting because it leverages more of the modern Python language features than any other3h 53m
Frequently asked questions
What are the prerequisites for this course?
This course is designed for learners of all backgrounds and skill levels. While prior experience with Python or general programming can be helpful, it is not a strict requirement. The course starts with foundational topics such as signing up for an OpenAI account and understanding API keys, making it accessible to beginners interested in AI-driven development.
What projects will I build during the course?
Throughout the course, you will engage in various projects that apply OpenAI's APIs. Notably, you will build a Color Palette Generator using the OpenAI Completion API and a Chatbot using the Chat API. These projects will involve setting up a Flask server, integrating OpenAI with the server, and creating functionalities like rendering color swatches and adding chatbot personalities.
Who is the target audience for this course?
The course is intended for anyone interested in artificial intelligence and AI-driven development, ranging from beginners to those with some programming experience. It provides a comprehensive introduction to using OpenAI's APIs, making it suitable for learners who want to create applications using GPT-4, DALL-E, and Whisper.
How does this course compare in depth and scope to other AI courses?
This course offers a project-driven approach to mastering OpenAI's APIs, focusing on practical applications like building a chatbot and a color palette generator. It covers a wide range of topics, from understanding tokens and model performance to specific prompt engineering techniques. The course's hands-on nature distinguishes it from more theory-focused AI courses.
What specific tools or platforms will I learn to use?
You will learn to use OpenAI's APIs, including GPT-4, DALL-E, and Whisper. The course also involves setting up a Flask server for integrating OpenAI functionalities, using TikToken for counting tokens, and employing various prompt engineering techniques to control and refine AI output.
What topics are not covered in this course?
The course does not cover advanced topics such as neural network architecture design, training custom AI models, or deep learning frameworks like TensorFlow or PyTorch. It focuses on utilizing pre-trained models and APIs rather than developing AI models from scratch.
How much time should I expect to commit to this course?
The course consists of 111 lessons and is designed to be immersive and hands-on. While the total runtime is not specified, students should anticipate a significant time commitment to engage with the projects and fully understand the range of topics covered, including prompt engineering, server integration, and OpenAI API usage.