Learn OpenAI GPT & AI Development by Building a GPT3.5/4 App

🔨 Project Based: In this course, you will learn how to build an AI assistant powered by OpenAI's GPT technology, 🤗HuggingFace, and Streamlit. You will start with a blank app, and add features one at a time.

📜 Concepts and Theory Explained in Plain English: Before adding a new feature, you will learn just enough theory to confidently build your app. You will learn the foundational concepts of GPT and generative AI, such as Large Language Models, Prompt Engineering, Semantic Search, Finetuning, RAG, and more.

💚 AI/ML Beginner Friendly: This course is designed to be beginner friendly, so you don't need to be an experienced AI developer. You will start with a blank app, and add features one at a time. The only prerequisite is Python and Pandas knowledge.

💾 Full Project Code + Colab Notebooks Included: You will get the full project source for the projects that include real-world code samples of using OpenAI’s APIs and their best practices. You will also get all Google colab notebooks, and access to the Q&A forum if you get stuck.

How It Works

In this course, we'll take a project-based approach. We will start with a blank app, and add new features in 5 mini-projects. Before each mini-project, we will go over the theoretical concepts and mental models needed to implement new features. Each project will all build upon each other into a complete AI application. And in doing so, you'll learn what theory is applied to what situation.

🤖 Phase 1: We will start by cloning ChatGPT using a Python based web app framework called Steamlit, then integrate with OpenAI's completion API. We will learn about Streamlit's high level features.

🗺️ Phase 2: We will apply our knowledge of prompt engineering to build and evaluate an IntentClassifier to solve classification and entity extraction problems. This will allow the chatbot to differentiate and route user prompts.

📰 Phase 3: We will have our AI assistant fetch stock news articles based on semantic search The news articles could come from any source. This example will teach you how to integrate chatbots with custom datasets, and search using sentence embeddings. You will work extensively with embedding algorithms and vector databases.

💹 Phase 4: We will have our AI assistant answer stock questions such as "Why is Tesla selling off". You will learn about Retrieval-Augmented-Generation and various strategies to handle hallucinations and increase reliability of your app.

👔 Phase 5: We will have our AI assistant plot stock charts and summarize earnings transcripts. This example will show you how to let chatbots use external tools and systems.

Intended Audience

Intermediate+ Python developers who are interested in coding a GPT based NLP app, from ground-up, and wants to pick up the foundational concepts about working with language models.

Prior Python and Pandas experience: The ability to follow along Python is needed.

No prior ML experience needed: This course explains every concept in as plain English as possible. Some prior AI / ML exposure is helpful, but not necessary.

Only Python is used, no Javascript knowledge necessary: both the front end and the back end for our projects is going to be written in pure Python.

What's Included

In addition to slide presentations:

Code-samples & line-by-line explanations: You will get all the code samples, including Google colab notebooks

Q&A forum access: You can post questions onto the community Q&A forum to get help where you get stuck.

Full Solution Source Code: Yes, you will get the ENTIRE source code for the project so you can deploy it yourself