The Complete OpenAI and GPT Course in Python w/ Q&A Chatbot

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Next Word AI Development & Training

5:13:29

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1 - Introduction

1 - Introduction.mp4

02:20

2 - Course Logistics and Important Announcements.mp4

02:57

3 - What We Are Building Problem Statement.mp4

05:30

4 - FAQ.html

5 - Important Disclaimers.html

2 - Project 0 Create a ChatGPT Clone with Python and Streamlit

6 - Course Setup.mp4

06:18

7 - Course Project Solutions.html

7 - link to the final project solution files zip.zip

8 - Building a ChatGPT Clone in 50 lines of Code.mp4

10:39

9 - Integrating OpenAI.mp4

06:02

3 - GPT3 Prompt Engineering and LLMs

10 - ChatGPT GPT3 InstructGPT How They Work.mp4

12:48

11 - Prompt Engineering and Advanced GPT Parameters.mp4

14:53

12 - Why GPT Disrupted AI Industry.mp4

08:32

4 - Project 1 Intent Classifier

13 - IntentClassifier What It is Why Its Important.mp4

04:29

13 - colab notebook.zip

14 - Prompts for Classification Problems Notebook.mp4

12:48

14 - colab notebook.zip

15 - Evaluation GPT35 for Classification Notebook.mp4

20:01

15 - colab notebook.zip

16 - Integrate Intent Classifier into the App.mp4

11:40

5 - Limits of GPT What It Cant Do

17 - Limitations of GPT Knowledge Cutoff Data Gaps Token Limits.mp4

11:14

18 - Limits of GPT Reasoning Chain of Thought Prompting.mp4

07:24

6 - Project 2 Semantic Search and Retrievers

19 - Semantic Search Based Retrieval.mp4

13:00

20 - Word and Sentence Embeddings Notebook.mp4

13:15

20 - colab notebook word and sentence embeddings.zip

21 - Semantic Search Notebook.mp4

12:57

21 - colab notebook semantic search.zip

22 - Vector Databases Pinecone Nearest Neighbor Search.mp4

17:16

22 - colab notebook semantic search.zip

23 - Integrating News Article Retriever into App.mp4

14:50

7 - Project 3 Retriever Augmented Question Answering and Fine Tuninng

24 - Question Answering with GPT and Finetuning GPT Models.mp4

17:34

25 - Question Answering Strategies for Handling Hallucinations Notebook.mp4

14:42

25 - colab notebook.zip

26 - Question Answering and Finetuning GPT Notebook.mp4

14:29

26 - colab notebook.zip

27 - Generative Labeling Finetuning GPT Model Evaluation Notebook.mp4

10:25

27 - colab notebook.zip

28 - App Integration.mp4

05:49

8 - Project 4 Summarization External System Integration

29 - Document Summarization with GPT.mp4

04:36

30 - Summarization with GPT Notebook.mp4

18:59

30 - colab notebook.zip

31 - Adding Real Time Financial Charts Notebook.mp4

09:32

31 - colab notebook.zip

32 - App Integration.mp4

06:01

33 - Deployment.mp4

02:29

Description

Master OpenAI's GPT, Semantic Search, Embeddings, and Q&A to build a Financial Assistant. Beginner friendly.

What You'll Learn?

The foundations of GPT and generative text - Large Language Models (LLM), Prompt Engineering
Receiver Augmented Generation (RAG) for Question Answering - its use cases and challenges, and real world implementation
Finetuning GPT models and their best practices, when and when not to fine tune.
Best practice strategies for troubleshooting issues with OpenAI APIs
Semantic Search - theory and Implementation
Vector databases, Pinecone - how they work, code samples
How to choose the right GPT model for completion and classification tasks
Understand how to use OpenAI’s APIs and their production best practices
Tackling the LLM hallucination problem - what the problem is, and specific strategies to mitigate it.

Who is this for?

Python developers with some Pandas experience who are eager to build their first AI app using GPT library

More details

Description
Note: This course assumes that you have gotten the basics of Python and Pandas down. You donâ€™t need to be an experienced Python and Pandas developer, but the ability to follow along and understand syntax is needed.

Take your AI development skills to the next level with this course!

In this course, you will learn how to build an AI assistant powered by OpenAI's GPT technology, HuggingFace, and Streamlit. In addition, you will learn the foundational concepts of GPT and generative AI, such as Large Language Models, Prompt Engineering, Semantic Search, Finetuning, and more. You will also understand how to use OpenAIâ€™s APIs and their best practices, with real world code samples.

Unlike other courses, you will learn by doing. You will start with a blank app, and add features one at a time. Before adding a new feature, you will learn just enough theory to confidently build your app.

You will get all the code samples, including Google colab notebooks, and access to the Q&A forum if you get stuck. You donâ€™t need a powerful PC or Mac that has GPUs to take this course. By the end of the course, you will be able to deploy and create your first app using OpenAIâ€™s technology, and be confident about the theoretical knowledge behind this technology. So sign up today and start building your AI powered app!

What you will learn:
Creating an AI chatbot with Streamlit
IntentClassifiers - what they are, how to build it.
Prompt Engineering: different ways of crafting the perfect prompt
How to evaluate and choose the best prompt
The concept of word embeddings
How to use word embeddings to quantify semantic similarity
How to use a vector database to store word embeddings
How to create a search engine that searches based on word embeddings
How to perform entity resolution for documents
Sentiment extraction using GPT
How to clean a finance dataset for use in a semantic search
How to embed finance documents and upload them to a vector database
How to use a language model to generate answers to questions
How to use fine-tuning to ensure the language model does not hallucinate
How to deploy a Q&A bot and a custom action system.
Who this course is for:
Python developers with some Pandas experience who are eager to build their first AI app using GPT library

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At NextWord, we help companies and developers bring AI and LLM technology (OpenAI, HuggingFace, Cohere) to market. We provide on-site and online training on GPT APIs through immersion days and 1 week sprints. NextWord is founded by industry veterans in conversational AI, including former Product Lead at Amazon Alexa, architect of Alexa mobile SDK, and a former Computer Science lecturer at University of Washington.

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