Learn to use the OpenAI API for chat completions, image generation, text-to-speech, and speech-to-text so that you can utilize AI technologies in your applications. Plus you'll build your own AI application for creating healthy daily meal plans.
The OpenAI API: GPT, DALL-E & Whisper
3h 43m 36s
English
Paid
About the Author: Zero To Mastery
Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.
The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.
The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.
Watch Online 36 lessons
0:00
/ #1: Introduction
All Course Lessons (36)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 03:13 | |
| 2 | Setting Up Jupyter Notebook | 14:15 | |
| 3 | Creating an OpenAI Account and an API Key | 03:52 | |
| 4 | Installing OpenAI API Authenticating to OpenAI | 08:42 | |
| 5 | OpenAI Models | 06:36 | |
| 6 | Making GPT Requests Using OpenAI API | 11:20 | |
| 7 | System Role | 04:18 | |
| 8 | OpenAI API Costs | 03:52 | |
| 9 | Tokens | 04:03 | |
| 10 | Chat Completion API Parameters: Temperature and Seed | 06:14 | |
| 11 | Chat Completion API Parameters: Top P, Max_Tokens, Penalties | 09:50 | |
| 12 | The Playground | 05:16 | |
| 13 | How OpenAI GPT Models Work | 09:52 | |
| 14 | LLMs Issues and Limitations | 06:22 | |
| 15 | Intro to Prompt Engineering | 02:41 | |
| 16 | Tactic 1: Position Instruction Clearly with Delimiters | 04:13 | |
| 17 | Tactic 2: Provide Detailed Instructions for the Context | 06:38 | |
| 18 | Tactic 3: Use the Rich Text Format (RTF) | 07:46 | |
| 19 | Tactic 4: Few Shot Prompting | 08:13 | |
| 20 | Tactic 5: Specify the Steps Required to Complete a Task | 05:17 | |
| 21 | Tactic 6: Give Models Time to Think | 02:13 | |
| 22 | Other Tactics and Principles for Better Prompting | 05:38 | |
| 23 | Avoid Hallucinations Using Guarding | 03:07 | |
| 24 | Summary | 02:07 | |
| 25 | Creating Variations of Images with DALL-E | 03:05 | |
| 26 | Generating Original Images Using the DALL-E | 10:34 | |
| 27 | Editing Images with DALL-E | 05:40 | |
| 28 | Transcriptions with Whisper | 05:48 | |
| 29 | Translations with Whisper | 03:12 | |
| 30 | Text-to-Speech (TTS) API | 07:03 | |
| 31 | Project Introduction | 02:32 | |
| 32 | Creating a Daily Meal Plan Using OpenAI API | 05:39 | |
| 33 | Creating the Prompt | 08:43 | |
| 34 | Running the Program | 03:24 | |
| 35 | Generating Original Images for the Recipes using DALL-E | 11:54 | |
| 36 | Narrate the Meals using the Text-to-Speech Model | 10:24 |
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Frequently asked questions
What are the prerequisites for enrolling in this course?
Before enrolling in this course, students should have a basic understanding of programming and be comfortable using tools like Jupyter Notebook. Familiarity with APIs and concepts of artificial intelligence will be beneficial but not mandatory. The course covers setting up an OpenAI account and using API keys, so prior experience with OpenAI's platforms is not required.
What kind of projects will I build during the course?
Students will build an AI application focused on creating healthy daily meal plans. This project involves using the OpenAI API to generate meal plans, creating prompts, running the program, generating images for recipes using DALL-E, and narrating meals with the Text-to-Speech model. It's a practical application of the concepts learned throughout the course.
Who is the intended audience for this course?
The course is designed for developers and tech enthusiasts interested in integrating AI technologies into their applications. It is suitable for those looking to understand and utilize the capabilities of OpenAI's GPT, DALL-E, and Whisper models. The course is also ideal for anyone keen on exploring prompt engineering and understanding AI model functionalities.
What specific tools or platforms will I learn to use in this course?
Students will learn to use the OpenAI API, which includes models like GPT for chat completions, DALL-E for image generation, and Whisper for audio tasks like transcription and translation. The course also covers using Jupyter Notebook for development, and students will engage with the API's various parameters and functionalities.
What topics are not covered in this course?
The course does not cover the detailed inner workings of AI model training or the development of AI models from scratch. It focuses on using pre-existing OpenAI models via the API. Advanced AI topics, such as deep learning frameworks and custom model training, are beyond the scope of this course.
How does the depth of this course compare to similar courses?
This course provides a focused exploration of the OpenAI API and its practical applications, emphasizing prompt engineering and specific model uses like GPT, DALL-E, and Whisper. It is particularly application-oriented, with hands-on projects, unlike other courses that might delve deeper into theoretical aspects or broader AI concepts.
What is the estimated time commitment to complete this course?
While the course consists of 36 lessons and offers a comprehensive overview of using the OpenAI API, the time commitment will vary based on individual pace and prior familiarity with the subject matter. Students should allocate sufficient time for hands-on projects and exercises to gain practical experience alongside the theoretical lessons.