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 📱Follow Us on Social Media: Facebook: https://www.facebook.com/datacampinc/ Twitter: https://twitter.com/datacamp LinkedIn: https://www.linkedin.com/school/datacampinc/ Instagram: https://www.instagram.com/datacamp/ #OpenAI #ChatGPT #AIApplications #PromptEngineering #APITutorial #Whisper #DataScience #MachineLearning #Embeddings #AIChatbots #FunctionCalling #OpenAIAPI