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

Empower Your Business With GenAI, Artificial Intelligence, Machine Learning, & Data Science

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Course Data

Lectures: 63
Length: 10h 39m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course Description

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

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.



4 Lectures · 15min
  1. Introduction and Outline (07:34) (FREE preview available)
  2. Where to get the code (02:06)
  3. How to Succeed in this Course (03:04)
  4. UPDATE: GPT-4o mini (03:02)

The ChatGPI and OpenAI API

19 Lectures · 02hr 34min
  1. The ChatGPI and OpenAI API Section Outline (03:59)
  2. Signup For OpenAI & Get Your API Key (06:28)
  3. Set Environment Variables (Windows) (02:38)
  4. Set Environment Variables (Linux & Mac) (06:21)
  5. Install the OpenAI and Tiktoken Libraries (01:15)
  6. Pricing (13:46)
  7. How to Pay & Add Billing Details (04:11)
  8. Quick Start (Use the ChatGPT API Right Away!) (13:55)
  9. What is a Token? (11:03)
  10. Estimating Costs with Tiktoken (14:20)
  11. Reproducibility (04:51)
  12. System Prompt (15:27)
  13. Incorporating History (17:40)
  14. Temperature (19:08)
  15. Frequency and Presence Penalties (03:35)
  16. How LLMs Conquered NLP (07:16)
  17. How LLMs Change the NLP & Machine Learning Workflow (03:20)
  18. Usage Policies (02:24)
  19. Suggestion Box (03:10)

Prompt Engineering & Applications

10 Lectures · 02hr 00min
  1. Prompt Engineering Section Outline (03:33)
  2. Unstructured to Structured (21:57)
  3. Structured to Unstructured (11:03)
  4. JSON Mode (08:07)
  5. Translation, Tone Enhancement, and Language Detection (19:53)
  6. Sentiment Analysis & Stock Trading (09:55)
  7. Summarization & ELI5 (10:01)
  8. Another ELI5 Example (03:59)
  9. Question-Answering (14:15)
  10. Chain of Thought Prompting (17:52)

Retrieval Augmented Generation (RAG)

7 Lectures · 01hr 05min
  1. What is Semantic Search? (17:34)
  2. Facebook AI Similarity Search (FAISS) (04:53)
  3. OpenAI's Embeddings Endpoint (02:01)
  4. RAG with FAISS (pt 1) (13:31)
  5. RAG with FAISS (pt 2) (08:07)
  6. RAG with FAISS (pt 3) (16:54)
  7. RAG with FAISS (pt 4) (02:28)


5 Lectures · 01hr 03min
  1. What Is Fine-Tuning and When Should You Use It? (13:53)
  2. OpenAI's Fine-Tuning and File Endpoints (06:51)
  3. Fine-Tuning ChatGPT - Making the Dataset (24:03)
  4. Fine-Tuning ChatGPT - Upload Data and Create Fine-Tuning Job (10:34)
  5. Fine-Tuning ChatGPT - Check the Results (08:12)

GPT-4 With Vision (VIP)

5 Lectures · 01hr 03min
  1. GPT-4 With Vision Concepts (09:28)
  2. GPT-4 With Vision Applications in Code (26:28)
  3. Application: Iterative Web Design (01:42)
  4. Analyze Video With GPT-4 (21:24)
  5. How To Use GPT-4o (03:59)

Background Info (Optional)

2 Lectures · 21min
  1. The Roots of Semantic Search (16:00)
  2. Who Should Take This Course? (05:19)

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

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

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

2 Lectures · 23min
  1. Proof that using Jupyter Notebook is the same as not using it (12:29)
  2. How to use Github & Extra Coding Tips (Optional) (11:12)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:49)
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