Generative AI- OpenAI API, ChatGPT, and GPT-4 in Python

Generative AI: OpenAI API, ChatGPT, and GPT-4 in Python, available at $54.99, has an average rating of 4.75, with 64 lectures, based on 96 reviews, and has 1153 subscribers.

You will learn about How to setup and use the OpenAI API with ChatGPT How to effectively use prompt engineering RAG (retrieval-augmented generation) with the OpenAI Embeddings API FAISS (Facebook AI Similarity Search) How to Fine-Tune ChatGPT This course is ideal for individuals who are Any level (beginner to expert) of student who wants to use the OpenAI API or Professionals who want to integrate ChatGPT automation into their business or Entrepreneurs who want to start their own AI business (customer service bots, AI girlfriends / AI boyfriends, etc.) It is particularly useful for Any level (beginner to expert) of student who wants to use the OpenAI API or Professionals who want to integrate ChatGPT automation into their business or Entrepreneurs who want to start their own AI business (customer service bots, AI girlfriends / AI boyfriends, etc.).

Enroll now: Generative AI: OpenAI API, ChatGPT, and GPT-4 in Python

Summary

Title: Generative AI: OpenAI API, ChatGPT, and GPT-4 in Python

Price: $54.99

Average Rating: 4.75

Number of Lectures: 64

Number of Published Lectures: 59

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 59

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

How to setup and use the OpenAI API with ChatGPT

How to effectively use prompt engineering

RAG (retrieval-augmented generation) with the OpenAI Embeddings API

FAISS (Facebook AI Similarity Search)

How to Fine-Tune ChatGPT

Who Should Attend

Any level (beginner to expert) of student who wants to use the OpenAI API

Professionals who want to integrate ChatGPT automation into their business

Entrepreneurs who want to start their own AI business (customer service bots, AI girlfriends / AI boyfriends, etc.)

Target Audiences

Any level (beginner to expert) of student who wants to use the OpenAI API

Professionals who want to integrate ChatGPT automation into their business

Entrepreneurs who want to start their own AI business (customer service bots, AI girlfriends / AI boyfriends, etc.)

Welcome to the forefront of artificial intelligence with our groundbreaking course on Generative AI (GenAI), the OpenAI API, and the ChatGPT API. With ChatGPT and GPT-4, you’ll learn how to build with the world’s most advanced Large Language Models (LLMs). 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:

1. 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.

2. 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!

3. Retrieval Augmented Generation (RAG): Learn how to incorporate external data into LLMs. 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”.

4. Fine-Tuning: Learn how to “train” ChatGPT on your own dataset so that it behaves the way you want it to. Sometimes prompt engineering and RAG won’t cut it.

5. GPT-4 with Vision: Everything in this course can be done with GPT-4, but what makes GPT-4 (and GPT-4 Turbo) special is its vision capabilities. That is, it can understand images. In this section, we’ll explore many of the amazing applications of combined text-image understanding, some of which include automated homework grading, explaining memes and humor, handwriting transcription, web development, game development, and writing product descriptions based on images (business owners – you already know how this will skyrocket your productivity).

Throughout this course, you’ll engage in hands-on exercises, real-world applications, and expert guidance to solidify your understanding and mastery of generative AI concepts. Whether you’re a seasoned developer, aspiring AI enthusiast, or industry professional, this course offers a transformative experience that will empower you to harness the true potential of AI.

Are you ready to embark on this exhilarating journey into the future of AI? Join us and unlock the endless possibilities of Generative AI today!

Suggested Prerequisites:

Python coding

Course Curriculum

Chapter 1: Welcome!

Lecture 1: Introduction and Outline

Lecture 2: Where To Get the Code

Lecture 3: How to Succeed in this Course

Chapter 2: The ChatGPI and OpenAI API

Lecture 1: The ChatGPI and OpenAI API Section Outline

Lecture 2: UPDATE: GPT-4o mini

Lecture 3: Signup For OpenAI & Get Your API Key

Lecture 4: Set Environment Variables (Windows)

Lecture 5: Set Environment Variables (Linux & Mac)

Lecture 6: Install the OpenAI and Tiktoken Libraries

Lecture 7: Pricing

Lecture 8: How to Pay & Add Billing Details

Lecture 9: Quick Start (Use the ChatGPT API Right Away!)

Lecture 10: What is a Token?

Lecture 11: Estimating Costs with Tiktoken

Lecture 12: Reproducibility

Lecture 13: System Prompt

Lecture 14: Incorporating History

Lecture 15: Temperature

Lecture 16: Frequency and Presence Penalties

Lecture 17: How LLMs Conquered NLP

Lecture 18: How LLMs Change the NLP & Machine Learning Workflow

Lecture 19: Usage Policies

Lecture 20: Suggestion Box

Chapter 3: Prompt Engineering & Applications

Lecture 1: Prompt Engineering Section Outline

Lecture 2: Unstructured to Structured

Lecture 3: Structured to Unstructured

Lecture 4: JSON Mode

Lecture 5: Translation, Tone Enhancement, and Language Detection

Lecture 6: Sentiment Analysis & Stock Trading

Lecture 7: Summarization & ELI5

Lecture 8: Another ELI5 Example

Lecture 9: Question-Answering

Lecture 10: Chain of Thought Prompting

Chapter 4: Retrieval Augmented Generation (RAG)

Lecture 1: What is Semantic Search?

Lecture 2: Facebook AI Similarity Search (FAISS)

Lecture 3: OpenAIs Embeddings Endpoint

Lecture 4: RAG with FAISS (pt 1)

Lecture 5: RAG with FAISS (pt 2)

Lecture 6: RAG with FAISS (pt 3)

Lecture 7: RAG with FAISS (pt 4)

Chapter 5: Fine-Tuning

Lecture 1: What Is Fine-Tuning and When Should You Use It?

Lecture 2: OpenAIs Fine-Tuning and File Endpoints

Lecture 3: Fine-Tuning ChatGPT – Making the Dataset

Lecture 4: Fine-Tuning ChatGPT – Upload Data and Create Fine-Tuning Job

Lecture 5: Fine-Tuning ChatGPT – Check the Results

Chapter 6: Background Info (Optional)

Lecture 1: The Roots of Semantic Search

Lecture 2: Who Should Take This Course?

Chapter 7: Setting Up Your Environment (Appendix/FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 8: Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

Lecture 1: Proof that using Jupyter Notebook is the same as not using it

Lecture 2: Where To Get the Code Troubleshooting

Lecture 3: How to use Github & Extra Coding Tips (Optional)

Chapter 9: Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Requ

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: What order should I take your courses in? (part 1)

Lecture 4: What order should I take your courses in? (part 2)

Chapter 10: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

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3 stars: 2 votes

4 stars: 41 votes

5 stars: 53 votes

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