dair-ai/ml-youtube-courses

★ 17,197Audience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((ML YouTube Courses))
    Machine Learning
      Caltech CS156
      Stanford CS229
      MIT 6.S897
    Deep Learning
      Karpathy Zero to Hero
      Stanford CS230
      CMU 11-785
    NLP
      Stanford CS224N
      Hugging Face course
      CMU Advanced NLP
    Other topics
      Computer Vision
      Reinforcement Learning
      MLOps and LLMs

mindmap root((ML YouTube Courses)) Machine Learning Caltech CS156 Stanford CS229 MIT 6.S897 Deep Learning Karpathy Zero to Hero Stanford CS230 CMU 11-785 NLP Stanford CS224N Hugging Face course CMU Advanced NLP Other topics Computer Vision Reinforcement Learning MLOps and LLMs

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Things people build with this

USE CASE 1

Find a free university-level deep learning course on YouTube to learn neural networks from scratch.

USE CASE 2

Browse NLP lectures from Stanford CS224N or Hugging Face without paying for a course platform.

USE CASE 3

Create a self-study plan for MLOps or prompt engineering using the linked YouTube playlists.

USE CASE 4

Compare what Stanford, MIT, CMU, and Caltech teach in their machine learning courses.

Getting it running

Difficulty · easy Time to first run · 5min

License not mentioned, this is a curated list of links to free public resources.

In plain English

This repository is not a piece of software but an organised directory of free machine learning and AI courses available on YouTube. It is maintained by DAIR.AI, a group that says it loves open AI education, and its purpose is to make it easier to find good, recent video courses on a wide range of ML topics in one place rather than hunting around YouTube yourself. The list is structured by subject area. Under Machine Learning you will find university courses such as Caltech CS156: Learning from Data, Stanford CS229, MIT 6.S897 on machine learning for healthcare, and probabilistic and statistical machine learning lectures. The Deep Learning section gathers Andrej Karpathy's Neural Networks: Zero to Hero series, Stanford CS230, MIT introductions to deep learning, CMU's 11-785, NYU's deep learning course, and others. There are dedicated sections for Natural Language Processing (including Stanford CS224N, Stanford CS25 on transformers, the Hugging Face NLP course, and CMU Advanced NLP), Computer Vision (including Stanford CS231N), Reinforcement Learning (with the DeepMind lecture series and Stanford CS234), Graph Machine Learning, Multi-Task and Meta-Learning, and practical topics such as MLOps, prompt engineering, LangChain, and full-stack LLM bootcamps. Each entry lists the lecture topics covered and links to the relevant YouTube playlist. Someone would actually use this if they want to teach themselves machine learning or a specific subfield like NLP or computer vision without paying for a platform, or if they want a quick way to see what is being taught at universities like Stanford, MIT, CMU and Caltech. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

Based on the ml-youtube-courses list, create a 12-week self-study plan to learn NLP using the linked free YouTube courses.

Prompt 2

Which courses in the ml-youtube-courses repo cover transformer architectures and attention mechanisms?

Prompt 3

I want to learn reinforcement learning from scratch, which entry in ml-youtube-courses should I start with and what topics does it cover?

Prompt 4

List all the practical applied courses in ml-youtube-courses covering MLOps, LLM apps, and LangChain with their YouTube links.

Prompt 5

Create a reading list to complement the Stanford CS229 machine learning course listed in ml-youtube-courses.

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