What it is

This is a free, community-curated study guide for learning artificial intelligence and machine learning. It collects links to free YouTube courses, online classes, blogs, and websites into one ordered roadmap.

It was created by Aadith Sukumar (GitHub user aadi1011) and covers AI, ML, deep learning, data science, natural language processing, computer vision, generative AI, reinforcement learning, and agentic AI.

Who it's for

Anyone interested in AI/ML, from complete beginners to people who want advanced material. The modules are ranked in increasing difficulty, so a beginner can start at Module 0 and work up, but the README says you can follow modules simultaneously or in order.

How to use it

The roadmap is split into 11 numbered modules plus a bonus module. Content marked with a ⭐ is what the author highly recommends. Each module is a table of resources (courses, videos, articles, software).

There are also two companion YouTube videos by the author that walk through the resources (Part 1 and Part 2).

The learning path (modules)

• Module 0 – Before You Start: Setting up your computer — installing Python 3.14, Visual Studio Code, the pip package installer, and common Python libraries used in AI/ML.
• Module 1 – The Math Behind It All: Foundational math — a math-for-ML playlist, a discrete mathematics course (NPTEL), discrete structures, MIT linear algebra lectures, and fundamental math for data science.
• Module 2 – Building Your Foundation: Computer science and Python programming — MIT's intro to CS with Python, Harvard's CS50 Python course, W3Schools Python intro, a 4-hour Python video, and HackerRank practice with a Python certification.
• Module 3 – Data Science: Learning about data — statistics and data science using Python, with a bonus 5-minute intro video, W3Schools data science intro, a Python-for-data-science video, Google Data Analytics certificate, and IBM Data Science certificate (⭐).
• Module 4 – Machine Learning: Teaching machines to learn from data — introductory ML articles, Harvard's data science ML course (⭐), GeeksForGeeks ML tutorial, a Microsoft Azure/OpenAI module, Andrew Ng's Machine Learning Specialization on Coursera (flagged as expert-level, recommended after you have fundamentals), and Google Cloud's ML engineer path.
• Module 5 – Computer Vision: Giving computers vision — crash course videos, an OpenCV full tutorial, OpenCV Bootcamp, Computer Vision Essentials (⭐), and an advanced Stanford lecture playlist.
• Module 6 – Deep Learning Neural Networks: How neural networks mimic the brain — DeepLearning.AI's course, a neural networks/deep learning course, convolutional neural networks course, a deep learning crash course (⭐), and a