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Hey there!

I'm a previous Software Engineer and I'm passionate about making complex ML concepts easy to understand.

Creator of GPT & Chill

I'm proud to have worked with creators like NeetCode and to have 3Blue1Brown endorse the GPT Learning Hub YouTube channel.

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And of course, the thousands of students who have enjoyed the GPT Learning Hub videos & practice problems.

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Check out the video to the right for a glimpse of my teaching style.

If you've found yourself here, I think you'll like it.

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I've packaged the lectures & problems into a free ten hour course, titled Generative LLMs. Drop your email below and I'll send over this Machine Learning Roadmap.

  • Is Generative LLMs really free?
    This course will always be free. It covers the foundations of modern Machine Learning and the key concepts that everyone should be familiar with. For those looking to dive even deeper and receive personalized instruction, I also offer an exclusive ML Community. However, I recommend having exposure to the concepts in Generative LLMs before joining the ML Community!
  • I'm interested in your ML Community. Do I need to complete this course first?
    While you don't need to finish the course, I would recommend having exposure to the topics in Generative LLMs before joining the ML Community. Many of the topics we teach in the ML Community build on topics from Generative LLMs. However, those looking to get questions answered 1:1 and receive guidance while completing Generative LLMs are welcome and encouraged to join!
  • What makes this course unique?
    For every lecture, I have created a free coding exercise. You can implement the concept (Linear Regression, Image Recognition, Transformers, etc.) in Python, and then run your code against the provided test cases! Implementing an ML concept is the best way to ensure a deep understanding.
  • How much math background is required?
    Most ML courses focus on an excessive amount of calculus. The only prerequisites to Generative LLMs are a basic understanding of Python, as well as basic calculus. For example, you should understand how to find the derivative of x². Extremely complicated derivatives are not actually required to build a strong intuition for ML algorithms! Submit your email below to start building your intuitive understanding of ML.
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Ace Your Interview
Practice Problems With Every Lecture
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Save Time & Learn Faster
Concise Lectures With The Right Balance of Math & Intuition
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Train Your Own GPT
Begin With ML Fundamentals & Build Up To Transformers 
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