OpenAI Developer Full Course | Master API & Function Calling, Prompt Engineering, Embeddings

Dive into OpenAI’s powerful AI capabilities with this comprehensive OpenAI Fundamentals course! Whether you’re a beginner or looking to refine your skills, this tutorial covers everything from interacting with the OpenAI API, crafting effective prompt engineering strategies, and integrating AI into real-world applications. Learn to build chatbots, analyze text, perform sentiment classification, and even use OpenAI’s Whisper for speech-to-text transcription.

By the end of this course, you’ll have hands-on experience developing AI-driven applications using Python, understanding API endpoints, and optimizing AI interactions for various business and development use cases.

🧠 What You’ll Learn:
Working with the OpenAI API: Understand authentication, API endpoints, and making requests.
Prompt Engineering for Developers: Master few-shot, zero-shot, and chain-of-thought prompting.
Text Processing with AI: Sentiment analysis, summarization, text transformation, and classification.
Building AI Chatbots: Implement ChatGPT-based conversational AI.
Speech-to-Text & Translations: Use OpenAI Whisper for transcribing and translating audio.
AI Agents & Function Calling: Integrate APIs, structure multi-turn conversations, and optimize response formats.
Embedding & Vector Databases: Leverage OpenAI’s embeddings to perform semantic search and recommendation systems.

📕 Video Chapters:
00:00 Introduction to OpenAI Fundamentals
00:18 Course Overview & Prerequisites
01:31 Understanding the OpenAI API
02:00 What is an API? Explained with Examples
03:37 Making API Requests & Authentication
05:24 Setting Up OpenAI’s Python Library
06:57 Exploring API Responses
08:30 Deep Dive into OpenAI’s AI Models
10:02 Controlling Randomness with Temperature
11:32 Text Transformations & Generations
12:32 Understanding Tokens & Costs
13:32 Classification Tasks Using OpenAI
15:29 Sentiment Analysis with Few-Shot Prompting
16:40 Unlocking Chat Capabilities & Roles
18:46 Multi-Turn Conversations & Memory
20:08 Storing Chat History for Better Context
22:29 Advanced AI Capabilities: Moderation & Transcription
24:06 Using OpenAI’s Moderation Model
27:17 Whisper Model for Speech-to-Text
29:29 Translating Audio with Whisper
30:26 Improving Accuracy with Context Prompts
33:21 Combining Models with Function Chaining
35:46 Extracting Meeting Insights Automatically
36:13 Introduction to Prompt Engineering
38:22 Key Principles for Crafting Effective Prompts
40:22 Structuring Outputs & Using Conditional Prompts
42:05 Few-Shot Prompting & In-Context Learning
47:16 Multi-Step Prompting for Complex Tasks
50:56 Chain-of-Thought Prompting for Better Reasoning
55:24 Self-Consistency Prompting for More Accurate Results
57:42 Iterative Prompt Engineering & Refinement
01:01:31 AI Applications: Text Summarization & Expansion
01:04:51 Text Transformation: Translation & Tone Adjustment
01:07:39 Grammar & Writing Improvement with AI
01:08:42 Text Analysis: Classification & Entity Extraction
01:12:04 AI for Code Generation & Explanation
01:15:52 Optimizing Chatbot Development with Prompt Engineering
01:19:29 Role-Playing Prompts for More Natural AI Interactions
01:23:17 Enhancing Chatbots with External Context
01:26:19 Best Practices for API Integration in Production
01:30:23 Handling API Errors & Rate Limits
01:38:16 Using Function Calling for Structured Outputs
01:44:45 Calling External APIs with OpenAI
01:51:07 Ensuring AI Safety & Moderation
01:56:40 Evaluating & Validating AI Model Performance
01:58:19 Key Takeaways for AI Safety & Ethics
02:02:13 Introduction to OpenAI Embeddings
02:07:08 Exploring Multi-Dimensional Embeddings
02:10:09 Visualizing Embeddings with t-SNE
02:12:41 Computing Similarities with Cosine Distance
02:16:52 Applications of Embeddings: Semantic Search
02:21:47 Building AI-Powered Recommendation Systems
02:26:04 AI-Powered Classification with Zero-Shot Learning
02:30:24 Introduction to Vector Databases
02:34:08 Choosing the Right Vector Database
02:35:05 Setting Up ChromaDB for AI Applications
02:39:15 Cost Considerations for Large-Scale AI Models
02:46:08 Advanced Querying & Filtering with Metadata
02:50:09 Course Summary & Next Steps

🖇️ Resources & Documentation:
OpenAI Fundamentals Skill Track: https://www.datacamp.com/tracks/openai-fundamentals

OpenAI API Docs: https://platform.openai.com/docs
OpenAI Pricing: https://openai.com/pricing
Guide to Prompt Engineering: https://www.datacamp.com/tutorial/prompt-engineering
Working with Embeddings: https://www.datacamp.com/tutorial/working-with-openai-embeddings
Whisper Speech-to-Text: https://platform.openai.com/docs/guides/whisper

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