OpenAI API & Integration Learning Path

Building Systems with the ChatGPT API

This course is designed to empower developers, this comprehensive guide provides a practical approach to integrating Langc Chain with OpenAI and effectively using Large Language Models (LL Ms) in Python.In the course's initial phase, you'll gain a robust understanding of what LangChain is, its functionalities and components, and how it synergizes with data sources and LL Ms. We'll briefly dive into understanding LL Ms, their architecture, training process, and various applications. We'll set up your environment with a hands-on installation guide and a 'Hello World' example using Google Colab.Subsequently, we'll explore the LangChain Models, covering different types such as LL Ms, Chat Models, and Embeddings. We'll guide you through loading the OpenAI Chat Model, connecting LangChain to Hugging Face Hub models, and leveraging OpenAI's Text Embeddings.The course advances to the essential aspect of Prompting & Parsing in LangChain, focusing on best practices, delimiters, structured formats, and effective use of examples and Chain of Though Reasoning (CoT).The following sections focus on the concepts of Memory, Chaining, and Indexes in LangChain, enabling you to handle complex interactions with ease. We will study how you can adjust the memory of a chatbot, the significance of Chaining, and the utility of Document Loaders & Vector Stores.Finally, you'll delve into the practical implementation of LangChain Agents, with a demonstration of a simple agent and a walkthrough of building an Arxiv Summarizer Agent.By the end of this course, you'll have become proficient in using LangChain with OpenAI LL Ms in Python, marking a significant leap in your developer journey. Ready to power up your LLM applications? Join us in this comprehensive course!

Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting AP Is like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to:Understand the fundamentals of AI and LangChain Set up your development environment from scratch Build and test working AI agents using Python and OpenAI Extend your agents with real-world tools and AP Is Join today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.Let me give you a quick rundown of what this course is all about:We’re going to bridge the gap between the basic CNNs architecture you already know and love, to modern, novel architectures such as VGG, Res Net, and Inception (named after the movie which by the way, is also great!)We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.In this course, you’ll see how we can turn a CNNs into an object detection system, that not only classifies images but can locate each object in an image and predict its label.You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.Another very popular computer vision task that makes use of CNNs is called neural style transfer.This is

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome to TensorFlow 2.0!What an exciting time. It's been nearly 4 years since TensorFlow was released, and the library has evolved to its official second version.TensorFlow is Google's library for deep learning and artificial intelligence.Deep Learning has been responsible for some amazing achievements recently, such as:Generating beautiful, photo-realistic images of people and things that never existed (GANs)Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)Self-driving cars (Computer Vision)Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)Even creating videos of people doing and saying things they never did (Deep Fakes - a potentially nefarious application of deep learning)TensorFlow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.In other words, if you want to do deep learning, you gotta know TensorFlow.This course is for beginner-level students all the way up to expert-level students. How can this be?If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.Along the way, you will learn about

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like Ada Grad, RM Sprop, and Adam which can also help speed up your training.Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going t

Unlock the Power of AI with LangChain: Learn to Create Revolutionary Language-Based Applications Looking to harness the full potential of AI and revolutionize the world of language-based applications? Look no further than LangChain, the comprehensive course designed to transform you from a novice to an expert in record time.Gen AI apps and LLM projects! Dive into hands-on projects that will shape your expertise, including:Project 1: Construct a dynamic question-answering application with the unparalleled capabilities of LangChain, OpenAI, and Hugging Face Spaces,  Google Gemini Pro .Project 2: Develop an engaging conversational bot using LangChain and OpenAI to deliver an interactive user experience.Project 3: Create an AI-powered app tailored for children, facilitating the discovery of related classes of objects and fostering educational growth.Project 4: Build a captivating marketing campaign app that utilizes the persuasive potential of well-crafted sales copy, boosting sales and brand reach.Project 5: Develop a ChatGPT clone with an added summarization feature, delivering a versatile and invaluable chatbot experience.Project 6: MCQ Quiz Creator App - Seamlessly create multiple-choice quizzes for your students using LangChain and Pinecone.Project 7: CSV Data Analysis Toll - Helps you analyze your CSV file by answering your queries about its data.Project 8: Youtube Script Writing Tool -  Effortlessly create compelling You Tube scripts with this user-friendly and efficient script-writing tool.Project 9 - Support Chat Bot For Your Website - Helps your visitors/customers to find the relevant data or blog links that can be useful to them. Project 10 - Automatic Tick

Master LangChain, OpenAI, Llama, Deep Seek and Hugging Face. Learn to Create hands-on generative LLM-powered applications with LangChain. Create powerful web-based front-ends for your LLM Application using Streamlit.By the end of this course, you will have a solid understanding of the fundamentals of LangChain OpenAI, Llama, Deep Seek and Hugging Face. You'll also be able to create modern front-ends using Streamlit in Python.Dive into hands-on projects that will shape your expertise, including:Project 1: Create a Simple Chatbot with Llama 2 and LangChain Project 2: PDF Chat App (GUI) | ChatGPT for Your PDF File - Streamlit Application to chat with your PDF file using LangChain and OpenAI.Project 3: You Tube Script Writing App - Effortlessly create title and script for the You Tube video using LangChain and OpenAI Project 4:  MCQ Quiz Creator App - Seamlessly create multiple-choice quizzes for your students using LangChain and OpenAI/ Hugging Face Project 5: Chat with Multiple PDF Documents | Streamlit Application- Chat with your PDF files using LangChain and OpenAI.Project 6: Support Chat Bot For Your Website - Helps your visitors/customers to find the relevant data or blog links that can be useful to them.Project 7: You Tube Video Summarizer - You Tube Video Summarizer, powered by the dynamic duo of LangChain and OpenAI! In this groundbreaking tool, we have harnessed the cutting-edge capabilities of language processing technology to transform the way you consume You Tube content.Project 8: Summarize PDF Using LangChain,  OpenAI  and Gradio:  Summarize PDF files using LangChain and OpenAI  and create a sharable web interface using Gradio Project 9: PrivateGPT- Chat with your Files Offline and FreeP

Welcome to the forefront of artificial intelligence with our groundbreaking course on Generative AI (GenAI), the OpenAI API, Deep Seek, and ChatGPT. With ChatGPT and Deep Seek, you'll learn how to build with the world's most advanced Large Language Models (LL Ms). This course is a must-have if you want to know how to use this cutting-edge technology for your business and work projects.This course contains 5 main sections:Basic API Usage: All the fundamentals: signup for an account, get your API key, set environment variables on Windows / Linux / Mac, using the API in Python, setup billing, understand the pricing model, and OpenAI's usage policies. Of note is the chatbot tutorial, which goes over how to incorporate chat history into the model so that ChatGPT "remembers" what it said to you previously. A customer service chatbot will serve as a running example throughout this course.Prompt Engineering: ChatGPT Prompt Engineering for Developers - All about how to make ChatGPT do what you want it to do. We'll explore various example use-cases, such as getting ChatGPT to output structured data (JSON, tables), sentiment analysis, language translation, creative writing, text summarization, and question-answering. We'll explore techniques like chain-of-thought (CoT) prompting, and we'll even look at how to use ChatGPT to build a stock trading system!Retrieval Augmented Generation (RAG): Learn how to incorporate external data into LL Ms. This powerful technique helps mitigate a common problem called "hallucination". It's critical if you have proprietary data (like product info for your company) that your LLM doesn't know about. You'll learn how semantic search / similarity search works, and how to implement it using FAISS (Facebook AI Similarity Search library). Learn how this will allow you to "chat with your data".Fine-Tuning:</stron

Welcome to “Build Powerful AI Agents: From LangChain to Local LL Ms”, your all-in-one course to become a complete AI Agent Engineer. Whether you’re a developer, data scientist, or AI enthusiast, this course will guide you step-by-step through building intelligent, multimodal, and voice-based AI systems — from the cloud (OpenAI) to local environments (Ollama &amp; MCP).By the end of this course, you’ll gain hands-on experience developing smart, interactive, and deployable AI agents that can think, talk, reason, and adapt — the same way top AI startups do it today.What You’ll Learn Understand how LangChain Agents work and how to integrate them with OpenAI AP Is.Build Voice-based Emotion and Wellness Companions using Whisper &amp; TTS.Create Virtual AI Talking Agents and Copilot Systems that perform autonomous tasks.Implement RAG-powered assistants using LLaMA 3.1 and Pinecone.Learn the basics of PyTorch for deep learning and model training.Explore Hugging Face and GPT models for NLP and dataset customization.Understand MCP (Model Context Protocol) and use FastMCP to connect LL Ms with databases.Master Fine-Tuning techniques and understand the difference between Fine-Tuning vs RAG.Deploy Local LL Ms (like Gemma and Qwen) using Google Colab + Ngrok for free hosting.Hands-on Labs and Implementations Enroll now and become the expert of Generative AI.

Willkommen zum ChatGPT-Kurs, der Ihnen alles beibringt, was Sie über die effektive Nutzung von ChatGPT wissen müssen! Dieser Kurs wurde entwickelt, um Ihnen die notwendigen Fähigkeiten zu vermitteln, um ChatGPT optimal zu nutzen, sei es für persönliche Projekte, berufliche Anwendungen oder kreative Ideen.Kursübersicht:Einführung in ChatGPT Verständnis der Grundlagen: Wie funktioniert ChatGPT?Überblick über die Anwendungsbereiche von ChatGPT in verschiedenen Branchen.Praktische Anwendungsfälle Schreiben von Texten: Tipps und Tricks zur Verbesserung der Textqualität.Kreative Anwendungen: Generierung von Ideen, Geschichten und mehr.Anpassung und Feinabstimmung Personalisierung von ChatGPT für Ihre speziellen Bedürfnisse.Feinabstimmung von Modellen für branchenspezifische Anforderungen.Integration von ChatGPT in Projekte Einbindung von ChatGPT in Ihre Arbeitswelt.Best Practices für die nahtlose Anwendung.Effiziente Kommunikation mit ChatGPT Richtiges Formulieren von Anfragen: Maximierung der Antwortqualität.Umgang mit Einschränkungen und Herausforderungen.Ethik und Verantwortung in der Nutzung von ChatGPT Sensibilisierung für mögliche Bias-Probleme.Verantwortungsbewusster Einsatz von KI-Technologien.Aktuelle Entwicklungen und Zukunftsaussichten Einblick in die neuesten Updates und Funktionen von ChatGPT.Ausblick auf zukünftige Entwicklungen in der Welt der KI-gesteuerten Kommunikation.Zielgruppe:Entwickler Content-Ersteller