Zero to AI - Free AI/ML Course: Python, LLMs, RAG, Agents & Deep Learning

Zero to AI is a free, open-source AI course and machine learning curriculum for learning Python, data science, deep learning, large language models (LLMs), retrieval-augmented generation (RAG), AI agents, prompt engineering, fine-tuning, and MLOps through 950+ hands-on Jupyter notebooks.

Use the live site for the guided learning experience at zero-to-ai.dev, and use this GitHub repo as the source curriculum, notebook library, and contribution hub.

Quick links: Website • Course Setup • Glossary • GitHub Repo

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Click any badge above to start coding in seconds!

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📊 Course Overview

From Zero to AI Mastery is still evolving. The core path is already usable, and feedback is welcome as later phases continue to grow.

This repository is best used as a guided curriculum, not as a directory to browse at random. Some phases are mature and portfolio-ready today, while a few later phases are still being filled in.

What You'll Learn

Area	Topics	Notebooks
Python & Data Science	NumPy, Pandas, Matplotlib, Scikit-learn	278+
Mathematics for ML	Linear algebra, calculus, statistics, optimization	40+
Tokenization & Embeddings	tiktoken, SentencePiece, OpenAI, HuggingFace	20+
Deep Learning	Neural networks, CNNs, RNNs, Transformers from scratch	45+
LLMs & RAG	Retrieval-Augmented Generation, vector databases, chunking	20+
AI Agents	Function calling, MCP, OpenAI Agents SDK, LangGraph, multi-agent	11+
Prompt & Context Engineering	Chain-of-thought, structured outputs, DSPy	6+
Fine-Tuning	LoRA, QLoRA, PEFT, GRPO, DPO	12+
MLOps & Serving	Deployment, monitoring, vLLM, quantization	15+
Evaluation & Safety	LLM-as-judge, red teaming, bias, fairness	12+
Advanced Topics	GANs, VAEs, RL, causal inference, time series	60+

✅ Start Here

The canonical reading experience is the live site at zero-to-ai.dev. The GitHub repo is the source of the curriculum (notebooks + MDX).

Zero install path: every notebook can run directly in your browser on the live site via Pyodide — no Python install, no credit card, works on a school laptop or a phone. Just open a phase and run the cells.

If you are new, use this order:

1. Open zero-to-ai.dev/00-course-setup for orientation.
2. Skim zero-to-ai.dev/23-glossary/01_GLOSSARY once before diving in.
3. Pick a path below, then a phase from the site sidebar.

If you are completely new to Python, begin with zero-to-ai.dev/01-python.

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🌍 Choose Your Path

Don't try to read all 33 phases. Pick one of these tracks based on your goal:

"I just want to use LLMs like ChatGPT effectively" — 4-6 weeks

01-python → 04-token → 11-prompt-engineering → 05-embeddings → 08-rag → 15-ai-agents

"I want a data science / ML job" — 4-6 months

01-python → 02-data-science → 03-maths → 06-neural-networks → 09-mlops → 28-practical-data-science → 16-model-evaluation

"I want to build production AI apps" — 3-4 months

11-prompt-engineering → 07-vector-databases → 08-rag → 15-ai-agents → 14-local-llms → 12-llm-finetuning → 09-mlops

"I'm a complete beginner, where do I start?" — start small

01-python (run cells in browser on the live site, no install). When you finish Python, come back and pick one of the tracks above.

"I have a slow laptop / limited internet" — works for you too

Run everything on zero-to-ai.dev directly. No GPU, no install, no Anaconda download. The browser does the work. For heavier notebooks (fine-tuning, large models), open them in Google Colab (most globally accessible free GPU).

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🌱 Getting Started

This comprehensive AI/ML curriculum uses progressive numbered modules covering everything from Python fundamentals to cutting-edge AI systems and advanced research topics. Each module includes hands-on notebooks, projects, and practical applications.

Important context for learners:

• The live site at zero-to-ai.dev is the canonical guided reading experience.
• Source content lives in two parallel trees: next-docs/src/app/<phase>/ (MDX, what the site renders) and jupyter-notebooks/<phase>/ (executable notebooks).
• Not every late-stage phase is equally complete yet. Phases such as 30-inference-optimization/ are still actively being built out.
• next-docs/.next/, next-docs/out/, and other generated folders are build artifacts, not the source curriculum.

Don't forget to:

• ⭐ Star this repo to find it easily later
• 🍴 Fork this repo to track your personal progress

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🚀 Quick Start

For most learners, the fastest low-friction path is:

1. Install dependencies (see options below).
2. Open the site at zero-to-ai.dev and pick a phase from the sidebar.
3. Open the matching notebook under jupyter-notebooks/<phase>/ and work through it.

What You Actually Need

• Basic Python knowledge (or start with zero-to-ai.dev/01-python).
• High school maths is enough for most of the course. Phases 3, 24, 25, and 27 go deeper if you want.
• Compute — pick whichever applies:
  • No laptop or low-spec laptop? Run notebooks in the browser on zero-to-ai.dev. Works on Chromebooks and phones. No GPU. No install.
  • 8 GB RAM, any OS? Fine for Phases 1-11 locally. Use Colab/Kaggle for the heavier later phases.
  • 16 GB+ RAM, any OS? Comfortable for most of the curriculum locally. Windows, macOS, and Linux all work — pyproject.toml documents the platform-specific install notes.
  • GPU? Optional. Needed only for local fine-tuning (Phases 12, 14, 24) and large inference (Phase 30). Free cloud GPUs cover those.
• Time: 3-5 hours/week if you're learning casually, 10-15 hours/week if you're studying for a job.

Installation (Choose One)

Option 1: UV - Fast & Recommended

# Clone the repository
git clone https://github.com/PavanMudigonda/zero-to-ai.git
cd zero-to-ai

# Install dependencies with UV (fastest!)
./install_dependencies.sh

# Start learning
jupyter notebook

Optional developer tooling for Phase 31:

# Install Node-based coding tools such as OpenCode
npm install

# Install the dedicated AI developer tools environment
# (used for OpenHands because it currently needs Python 3.12)
INSTALL_AI_DEV_TOOLS=1 ./install_dependencies.sh

Option 2: Conda - Best for ML/Data Science

# Clone the repository
git clone https://github.com/PavanMudigonda/zero-to-ai.git
cd zero-to-ai

# Create conda environment
conda env create -f environment.yml
conda activate aiml-learning

# Start learning
jupyter notebook

Option 3: Cloud (No Installation!)

Listed by global accessibility — the higher entries are easiest to use no matter where you are.

• Run on the live site (most accessible — no account, no credit card, no install, works on phones): Open any phase on zero-to-ai.dev and run the cells in-browser via Pyodide. No GPU; not every Python package has a Pyodide wheel. Best for early phases (Python, data science, prompt engineering) and any notebook that doesn't need PyTorch on a GPU.

• Google Colab (free, works in most countries, no credit card): Click - then run this in the first cell:

  !pip install -q -r https://raw.githubusercontent.com/PavanMudigonda/zero-to-ai/main/colab_requirements.txt
  

  Free GPU is available but rate-limited.

• Kaggle (free GPU, requires phone verification): Click [![Open in Kaggle](https://img.shields.io/badge/Open%20in-Kaggle-20BEFF?logo=