Published 10/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.59 GB | Duration: 6h 3m
Master AI App Development using OpenAI API integration, Prompt Engineering, Function Calling and Structured Outputs.
What you’ll learn
Learn to interact with OpenAI Platform (Generative AI) using Python Code
Learn the LLM basics, ChatGPT evolution, training, and practical usage.
Learn to work and explore the multimodal capabilities such as images, files, audio using OpenAI and Python code.
Learn to use Prompt Engineering to guide AI models in generating accurate outputs.
Learn to use latest techniques to generate the Structured Outputs from LLM
Learn to use the power of function calling with OpenAI to interact with external systems
Requirements
Experience with Python
Experience working with IDE such as Visual Studio Code.
Description
“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 CourseThis 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 & ChatGPTDive 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 MasteryMaster 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 OpenAILearn 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 OpenAIDelve 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 OpenAIExplore 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 EngineeringThis 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 OpenAIUnderstand 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 OpenAIDiscover 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.
Overview
Section 1: Getting Started With the Course
Lecture 1 Course Introduction
Lecture 2 Pre-requisites
Section 2: Course Slides and Source Code
Lecture 3 Course slides
Lecture 4 Source Code
Section 3: Introduction to Large Language Models (LLMs), OpenAI & ChatGPT[ Theory ]
Lecture 5 The World Before LLMs: A Glimpse into the Past?
Lecture 6 Large Language Models and its Evolution
Lecture 7 How are LLMs Models Trained ?
Lecture 8 GPT Models and its Evolution
Lecture 9 Advantages, Challenges and Applications using LLMs
Lecture 10 Sign up for a ChatGPT Account and Start Exploring
Section 4: OpenAI APIs: Your First Steps to Mastery
Lecture 11 Introduction to OpenAI API
Lecture 12 Setup OpenAI Account & Open AI Playground
Lecture 13 Setup python in Mac
Lecture 14 Setup python in Windows
Lecture 15 Set up the Base Project using Poetry
Lecture 16 Set up OpenAI APIKey
Lecture 17 Interact with GPT using OpenAI Client
Lecture 18 Structuring API Calls with Functions
Lecture 19 Prompt, Tokens and Tokenization – What are they ?
Lecture 20 OpenAI Request Parameters – temperature
Lecture 21 OpenAI Request Parameters – max_tokens
Lecture 22 OpenAI Request Parameters – top_p
Lecture 23 Streaming OpenAI Responses
Lecture 24 Understanding System, Assistant, and User Messages in OpenAI
Lecture 25 System, Assistant, and User Messages in Action
Section 5: Mastering Multimodality: Creating and Editing Images with OpenAI
Lecture 26 Introduction to MultiModality in AI
Lecture 27 Creating an Image using OpenAI
Lecture 28 Refactor Code to Write the Image in the file system
Lecture 29 Create an variation using the “create_variation” function
Section 6: Mastering Multimodality: Exploring Vision Capabilities with OpenAI
Lecture 30 Unlocking Vision: Image Understanding Capabilities using Image URL
Lecture 31 Unlocking Vision: Understanding Capabilities – using Encoded Image
Lecture 32 Vision API Limitations
Section 7: Mastering Multimodality: Creating and Processing Audio with OpenAI
Lecture 33 From Speech to Sound: Converting Text to Voice with OpenAI’s TTS Model
Lecture 34 Introduction to the Whisper API in OpenAI
Lecture 35 Transcribing Speech using Whisper API in OpenAI
Lecture 36 Translation using Whisper API in OpenAI – Translate from French to English
Section 8: Prompt Engineering
Lecture 37 Prompt Engineering & PromptTemplate
Lecture 38 Set up Project for Prompt Engineering
Lecture 39 Prompt Engineering in Action – Lets explore the Travel Plan Prompt
Lecture 40 Understanding Prompt Injection and How to Mitigate It
Lecture 41 Zero Shot Prompting
Lecture 42 Few Shot Prompting
Lecture 43 Chain of Thought Prompting
Lecture 44 Mastering Multi-Step Prompts
Section 9: Generating Structured Data with OpenAI
Lecture 45 Introduction to Structured Outputs in LLM
Lecture 46 Structured outputs using Prompt Engineering
Lecture 47 Structured outputs with Few Shot Examples using Prompt Engineering
Lecture 48 Pydantic in Action
Lecture 49 Structured outputs with Pydantic Model Validations using Prompt Engineering
Lecture 50 Structured outputs using response_format and pydantic
Section 10: Function Calling using tools with OpenAI
Lecture 51 Function Calling – What & Why ?
Lecture 52 Function Calling with OpenAI: Accessing System Name and Time
Lecture 53 Building an Interactive Command-Line App
Lecture 54 Connecting OpenAI Function Calling to Open Meteo API for Realtime Weather Data
Lecture 55 Real-time Stock Price Retrieval using OpenAI Function Calling
Software Developers looking to integrate AI capabilities into their applications using OpenAI and Python.,Data Scientists interested in enhancing their skill set with AI application development.,AI Enthusiasts who want to explore practical implementations of OpenAI’s APIs and ChatGPT.,Machine Learning Engineers aiming to expand their knowledge by incorporating language models into projects.,Entrepreneurs and Startups aiming to build AI-based products or services quickly and efficiently.,Students and Graduates in computer science or related fields who want hands-on experience with AI applications.,Anyone Curious About AI with a foundational understanding of Python and a desire to learn about AI application development.
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