
OpenAI APIs Pro with Python Mega: Build Generative AI Apps
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 3h 8m | Size: 1.7 GB
Build a real Generative AI application with Python and OpenAI-without getting lost in machine-learning theory.
This course is for:
Python beginners who want a practical GenAI portfolio project
Junior developers building their first OpenAI API application
Data analysts and automation specialists who want to automate text-based work
Freelancers who want to offer AI-powered workflow solutions
Technical creators who want to move beyond manual ChatGPT use
What you will learn:
Explain how LLMs and GPT models generate responses
Select a model using quality, speed, and cost criteria
Set up the OpenAI API securely in a Python project
Protect API keys using environment variables
Write prompts with role, task, context, constraints, and output format
Generate structured, reliable AI responses
Build practical text-generation and extraction workflows
Handle common API errors and invalid inputs
Test AI outputs with realistic user scenarios
Build and document a customer-support chatbot portfolio project
Requirements:
Basic Python knowledge: variables, functions, packages, and running scripts
A computer with Python installed
An OpenAI API account with available billing or credits
Curiosity and willingness to test, improve, and document your work
Final project:
You will build a Python customer-support chatbot that uses approved policy context, returns controlled responses, handles common failures, and includes ten test cases plus a professional README. By the end, you will have a shareable project that demonstrates practical GenAI application-development skills.
Who this course is for:
Python developers and data analysts who want to transition from basic scripting to building intelligent, scalable generative AI applications. It is also highly valuable for tech professionals frustrated by confusing AI documentation who need clear, structured engineering principles to confidently automate workflows and deploy AI features.


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