CF

OpenAI API with Python Bootcamp: ChatGPT API, GPT-4, DALL·E

9h 8m 16s
English
Paid
Updated September 2026

OpenAI API with Python Bootcamp: ChatGPT API, GPT-4, DALL·E is a 76-lesson 9 hours 8 minutes self-paced course by Udemy. OpenAI API with Python Bootcamp: ChatGPT API, GPT-4, DALL·E covers every major OpenAI model reachable via API — GPT-3, ChatGPT (GPT-3.

Course facts

Lessons
76
Duration
9 hours 8 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Udemy
Price
Premium

OpenAI API with Python Bootcamp: ChatGPT API, GPT-4, DALL·E covers every major OpenAI model reachable via API — GPT-3, ChatGPT (GPT-3.5-Turbo and GPT-4), DALL-E, and Whisper — through Python.

Seven Real Projects

Rather than isolated API calls, the course builds seven complete projects: zero-shot sentiment analysis, a ChatGPT clone, a meal-plan generator, a Linux sysadmin assistant (ShellGPT), a YouTube summary generator, and a book-recommendation system, among others.

What You'll Learn

  • Making requests to GPT-3, ChatGPT, and GPT-4 through the OpenAI API
  • Understanding tokens, completion parameters, and API cost management
  • Generating and editing images with DALL-E, and transcribing speech with Whisper
  • Text embeddings, prompt engineering, and building a Streamlit interface for your app

Across 76 lessons, the course is kept current as OpenAI's models and API evolve.

Who teaches OpenAI API with Python Bootcamp: ChatGPT API, GPT-4, DALL·E? Udemy

Udemy thumbnail

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.

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#1: How to Get the Most Out of This Course
All Course Lessons (76)
#Lesson TitleDurationAccess
1
How to Get the Most Out of This Course Demo
02:24
2
Setting Up the Environment: Jupyter Notebook and Google Colab
14:43
3
Creating an OpenAI Account and an API Key
03:57
4
Installing the OpenAI API Library and Authenticating to OpenAI
08:49
5
OpenAI Models
06:52
6
Making GPT-3 Requests Using the OpenAI API
07:31
7
Making ChatGPT Requests Using the OpenAI API
09:01
8
Diving into ChatGPT
07:36
9
Diving into GPT-4
09:22
10
OpenAI API Costs
03:38
11
Tokens
03:59
12
OpenAI Model Completion Parameters
12:33
13
ChatGPT System Role
04:44
14
Prompt Engineering
13:03
15
Image Generation Using the DALL-E Model
11:02
16
Using DALL-E to Create Variations and Edit Images
09:39
17
Diving into DALL-E
03:57
18
Speech Recognition With Whisper
08:12
19
Project Requirements
01:26
20
Building the Application
04:48
21
Testing the Application
08:22
22
Building a Front-End Using Streamlit
05:46
23
Creating the Web App Layout With Streamlit
07:48
24
Saving and Displaying the History Using the Streamlit Session State
05:26
25
Project Requirements
03:43
26
Making a Dialog With the AI Model
10:07
27
Looping
09:57
28
Testing the Project and Python Script Overview
09:33
29
Project Requirements
01:25
30
Creating the Meal Plan Using the ChatGPT API
07:36
31
Generating Meal Images Using DALL-E
10:40
32
YouTube Videos Summary Generator: Project Requirements
05:38
33
Building a YouTube Downloader With Python
13:56
34
The YouTube Download Function
05:45
35
Transcribing Using Whisper
05:29
36
Summarizing Using GPT
06:16
37
Testing the Application and Adding Enhancements
13:46
38
Coding a Simple Application: Password Generator
03:41
39
Coding an Intermediate-level Application with GPT-4: The Tetris Game
05:03
40
Coding a Complex Application with GPT-4: Voice Assistant
13:19
41
Intro to OpenAI's Text Embeddings
03:22
42
Generating Simple Embeddings
06:48
43
Finding Similarities Using Embeddings
13:45
44
Performing Semantic Searches
10:20
45
Project Introduction
03:38
46
Building the Application - Part 1
12:42
47
Building the Application - Part 2
09:52
48
Testing the Application
05:04
49
Visualizing Embeddings
04:57
50
Displaying Embeddings on Atlas
10:42
51
Project Introduction
04:09
52
Installing and Configuring ShellGPT
10:25
53
Using ShellGPT like a PRO
12:25
54
The Chat Feature of ShellGPT
08:14
55
While and continue Statements
04:18
56
While and break Statements
05:48
57
List Slicing and Iteration
07:27
58
List Comprehension - Part 1
06:16
59
List Comprehension - Part 2
06:40
60
Working with Dictionaries
10:40
61
JSON Data Serialization
06:41
62
JSON Data Deserialization
05:50
63
Assignment: JSON and Requests/REST API
02:00
64
Assignment Answer: JSON and Requests/REST API
03:59
65
Introduction to Streamlit
04:52
66
Streamlit Main Concepts
05:52
67
Displaying Data on the Screen: st.write() and Magic
05:49
68
Widgets, Part 1: text_input, number_input, button
05:22
69
Widgets, Part 2: checkbox, radio, select
07:47
70
Widgets, Part 3: slider, file_uploader, camera_input, image
10:39
71
Layout: Sidebar
02:11
72
Layout: Columns
06:01
73
Layout: Expander
02:22
74
Displaying a Progress Bar
04:10
75
Session State
09:22
76
Callbacks
07:15
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Frequently asked questions

What are the prerequisites for enrolling in this course?
The course does not list specific prerequisites, but a basic understanding of Python programming and familiarity with using APIs would be beneficial. The course begins with setting up the environment using Jupyter Notebook and Google Colab, suggesting that some prior knowledge of these tools may be helpful.
What projects will I build during the course?
Throughout the course, you will work on several projects, including a Zero-Shot Sentiment Analysis using ChatGPT, building a ChatGPT clone from scratch, creating a healthy daily meal plan with the ChatGPT API, and developing a YouTube Videos Summary Generator. Other projects include boosting Linux sysadmin capabilities with ShellGPT and a Books Recommendation System.
Who is the target audience for this course?
The target audience is individuals interested in integrating OpenAI models into their Python applications. This includes developers looking to enhance their skills in AI, data scientists, and tech enthusiasts keen on leveraging advanced AI capabilities such as GPT-4, DALL-E, and Whisper.
How does the depth of this course compare to similar courses?
This course offers a comprehensive exploration of the OpenAI API, covering a wide range of models, including GPT-3, GPT-4, DALL-E, and Whisper. With 76 lessons, it provides detailed hands-on experience, from basic setup to advanced projects like building a ChatGPT clone and a voice assistant application using GPT-4.
What specific tools or platforms does the course utilize?
The course uses Jupyter Notebook and Google Colab for development environments. It also involves using the OpenAI API library for model integration and Streamlit for building front-end applications. Additionally, tools like ShellGPT are covered to boost Linux sysadmin capabilities.
What topics are not covered in the course?
While the course covers a broad range of topics related to the OpenAI API and its models, it does not delve into other AI frameworks or libraries such as TensorFlow or PyTorch. The focus remains on OpenAI's specific models and their integration with Python applications.
What is the expected time commitment for this course?
The course comprises 76 lessons, and while the total runtime is not specified, the content ranges from setting up environments to complex project development. Prospective students should be prepared for a substantial time investment to fully engage with the material and complete all projects.