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"AI Application Development with OpenAI, ChatGPT, and Python" is a comprehensive course designed to teach you how to harness the power of OpenAI's APIs and tools to build advanced AI applications.
You'll explore the fundamentals of Large Language Models (LLMs), understand the evolution of ChatGPT, and gain hands-on experience in using OpenAI's capabilities for text, image, and audio processing.
The course covers essential topics such as prompt engineering, structured data generation, and function calling, enabling you to create dynamic and interactive AI solutions.
"AI Application Development with OpenAI, ChatGPT, and Python" is a comprehensive course designed to teach you how to harness the power of OpenAI's APIs and tools to build advanced AI applications.
You'll explore the fundamentals of Large Language Models (LLMs), understand the evolution of ChatGPT, and gain hands-on experience in using OpenAI's capabilities for text, image, and audio processing.
The course covers essential topics such as prompt engineering, structured data generation, and function calling, enabling you to create dynamic and interactive AI solutions.
Whether you're a developer, data scientist, or AI enthusiast, this course provides the knowledge and skills to develop cutting-edge AI applications using Python and OpenAI.
1. Getting Started with the Course
This section introduces the course, outlining what you can expect to learn and achieve by the end of it. We will cover how to set up your environment, download course materials, and access the resources needed to follow along.
2. Introduction to Large Language Models (LLMs), OpenAI & ChatGPT
Dive into the world of LLMs with an in-depth look at OpenAI's ChatGPT, its architecture, and how it’s revolutionizing AI-driven language processing. Explore the history of how we interacted with computers before LLMs and how it has evolved since the release of ChatGPT. You'll trace the evolution of LLMs and understand the complexities involved in training these models.
3. OpenAI APIs: Your First Steps to Mastery
Master the essential steps for working with OpenAI APIs, from setting up your environment on Mac or Windows to making your first API requests. This section covers everything from installing Python, managing dependencies using Poetry or pip, configuring your OpenAI API key, and interacting with GPT models using OpenAI clients.
4. Mastering Multimodality: Creating and Editing Images with OpenAI
Learn how to leverage OpenAI's capabilities to generate and edit images. This section introduces you to multimodality in AI, combining text and image generation. You’ll explore how to create images, edit them, and use OpenAI's variation functions to enhance creativity.
5. Mastering Multimodality: Exploring Vision Capabilities with OpenAI
Delve into the vision capabilities of OpenAI. You'll learn how to analyze images using URLs, process base64-encoded images, and understand the limitations of OpenAI’s Vision API. This knowledge will help you integrate vision-based AI solutions into your projects.
6. Mastering Multimodality: Creating and Processing Audio with OpenAI
Explore how OpenAI handles audio data, including text-to-speech conversion, speech-to-text transcription, and language translation using the Whisper API. You will gain hands-on experience in converting written text into speech and transcribing spoken language into text.
7. Prompt Engineering
This section covers the art of crafting prompts to guide AI models in generating accurate outputs. You’ll learn about various prompting techniques, including zero-shot and few-shot prompting, and how to structure prompts to achieve desired results. You’ll also explore how to protect prompts from injection attacks.
8. Generating Structured Data with OpenAI
Understand how to generate structured data using OpenAI's LLMs. This section includes prompt engineering techniques, using Pydantic for data validation, and advanced methods for structured outputs. You'll learn how to manage structured data in Python efficiently and how to combine Pydantic with prompt engineering for accurate data generation.
9. Function Calling using Tools with OpenAI
Discover how to use OpenAI for function calling to interact with external systems, retrieve real-time data, and build interactive applications. You'll learn how to connect OpenAI to APIs for real-time data, such as weather updates and stock prices, making your AI applications more dynamic and responsive.
This comprehensive course will equip you with the skills needed to build AI-powered applications using OpenAI's powerful tools and APIs. Join us as we embark on this journey to master the art of AI application development with hands-on projects and real-world examples.
This lecture explains the top_p parameter and its role in controlling response randomness.
In this lecture, I will give an introduction to the course and highlight the topics that are covered in this course.
In this lecture, I will cover the prerequisites that are needed for this course.
Explore key concepts visually with comprehensive course slides that provide an organized overview of the material.
Get hands-on experience with source code examples that bring the course content to life through practical implementation.
Explore the pre-LLM era, How we interact with computers for solutions ? How its changed since the release of ChatGPT.
Trace the evolution of Large Language Models, from early iterations to the cutting-edge architectures shaping today's AI landscape.
Delve into the training process of LLMs, covering data preprocessing, model training, fine-tuning, and the computational challenges involved.
Discover the journey of GPT models, from the inception of GPT-1 to the advancements in GPT-4, highlighting key innovations and improvements.
Examine the benefits, hurdles, and diverse applications of LLMs, showcasing their impact across various industries and use cases.
Learn how to sign up for ChatGPT, navigate its features, and begin experimenting with this powerful AI tool in practical scenarios.
This lecture provides an overview of the OpenAI API and its key capabilities.
Learn how to create an OpenAI account and explore the capabilities of the OpenAI Playground.
This lecture guides you through setting up Python on a Mac for OpenAI API development.
Learn how to install and configure Python on Windows for working with OpenAI APIs.
Discover how to initialize a base project for OpenAI API using Python’s Poetry dependency manager.
This lecture shows how to securely configure your OpenAI API key in your project.
Learn how to interact with GPT models by sending requests through the OpenAI client.
This lecture focuses on techniques to refactor and improve the readability of your OpenAI API code.
Understand how temperature and max_tokens parameter influence the behavior of GPT responses.
Understand how max_tokens parameters influence the behavior of GPT responses.
Explore the concepts of prompts, tokens, and tokenization and how they impact API interactions.
Learn how to stream responses from OpenAI for real-time interaction with the API.
This lecture introduces the roles of system, assistant, and user messages in shaping AI responses.
See how system, assistant, and user messages work in practice through real-world examples.
An overview of multimodality in AI, integrating text and image generation for versatile applications
Learn how to create images using OpenAI’s image generation API with step-by-step guidance
In this lecture, you'll learn how to refactor your code to efficiently save images to the file system, ensuring clean and optimized handling of image data in your applications.
Discover how to create image variations using OpenAI's create_variation function for enhanced creativity.
This lecture explores how OpenAI processes images via URLs to generate descriptions, perform object recognition, and analyze visual content.
Learn how OpenAI models interpret base64-encoded images to extract visual features and process data directly within requests.
Examine the constraints and challenges associated with OpenAI's Vision API, including limitations in image size, processing, and capabilities.
Learn how to use OpenAI’s TTS model to convert written text into realistic, natural-sounding speech.
Discover how Whisper API can transcribe spoken language into accurate, readable text in various languages.
Understand how Whisper API handles translations, transforming spoken French into English with accuracy and ease.
This lecture teaches how to create clear, structured prompts that lead to accurate and meaningful AI outputs.
Learn how to set up your project environment, ensuring all tools and libraries are ready for effective prompt engineering.
Dive into a practical example by creating a travel plan prompt, showcasing prompt engineering techniques in a real-world scenario.
Explore the concept of prompt injection, its potential risks, and effective strategies to protect your prompts against manipulation.
This lecture explains zero-shot and few-shot prompting techniques to improve AI performance with minimal examples.
Learners will explore how Chain of Thought Prompting helps guide AI through logical, step-by-step problem-solving.
Learn how to craft and utilize multi-step prompts to guide LLMs through complex tasks, achieving more detailed and accurate responses.
Understand how LLMs generate structured data and why it's crucial for AI-driven applications.
Explore how prompt engineering can guide LLMs to create structured and organized outputs efficiently.
Learn how few-shot examples enhance prompt engineering to produce more accurate structured outputs.
Dive into Pydantic and see how it helps in defining, validating, and managing structured data in Python.
Discover how to combine Pydantic and prompt engineering for robust structured output generation and validation.
Learn to leverage response_format with Pydantic to generate well-structured, validated data effortlessly.
Understand the fundamentals of function calling and why it's essential for AI integration. Discover how OpenAI uses function calling to access and manipulate external data.
Learn how to use OpenAI's function calling to fetch system details like name and time. This lecture demonstrates how AI can interact with your device for dynamic information retrieval.
Create a command-line application that takes user input and iterates seamlessly. Understand how to implement function calling for dynamic, interactive experiences.
Integrate OpenAI function calling with the Open Meteo API to fetch real-time weather data. Gain hands-on experience in using AI to interact with external weather services.
Learn to connect OpenAI with financial APIs to retrieve live stock prices. This lecture showcases how to leverage AI for real-time financial data integration.
Get started with Streamlit by building a basic chatbot that interacts with users through a clean and simple UI.
Build a simple chatbot using Streamlit.
Upgrade your chatbot to stream responses for a real-time conversational experience.
Implement chat history to display previous interactions, enriching the user experience.
Explore OpenAI's Canvas and learn how to enhance chatbot interactions with a dynamic UI.
Implement a sidebar to keep track of chatbot conversations for a seamless user experience.
Get familiar with SQLite3 and understand its role in lightweight data storage.
Learn how to store chatbot conversations in an SQLite database for persistence.
Learn how to enable image processing in your chatbot, allowing users to upload and analyze images seamlessly.
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