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🔬 AI Scientist Roadmap

From Zero to AI Researcher in 12-18 Months

GitHub stars GitHub forks License: CC0 PRs Welcome

A complete, self-paced curriculum to become an AI Scientist. 100% free resources. Hands-on projects. Clear milestones.

🌱 Beginner → 🌿 Intermediate → 🌳 Advanced → 🔬 Scientist

Quick Start · Full Roadmap · Why This Roadmap · Contributing

🤔 Why This Roadmap?

There are many AI learning resources. Here's why this one is different:

Problem with other roadmaps This roadmap
Scattered resources, no structure Linear progression with clear phases
Paid courses hidden as "essential" 100% free resources for every topic
Theory-heavy, no practice Projects at every phase to build portfolio
Outdated (pre-ChatGPT era) Updated for 2024-2025 with LLMs, RAG, agents
No research path Phases 4-5 teach how to read/write papers
One-size-fits-all Specialization tracks (CV, NLP, RL, GenAI, Safety)

Who is this for?

  • Self-taught developers wanting to transition into AI
  • CS students looking for structured self-study
  • ML engineers wanting to move into research
  • Anyone curious about the path from code to papers

What you'll achieve

Phase 0-1: Can build ML models and understand the math
Phase 2:   Can train neural networks for real problems
Phase 3:   Specialist in CV, NLP, RL, GenAI, or AI Safety
Phase 4:   Can read papers and reproduce research
Phase 5:   Can produce original research

⚡ Quick Start

Don't have time to read everything? Start here:

Week 1: Validate your interest

  1. Watch 3Blue1Brown: Neural Networks (1 hour)
  2. Complete Kaggle's Intro to ML (3 hours)
  3. Train your first model in Google Colab

Week 2-4: Build foundation

  1. Start Andrew Ng's ML Course (audit free)
  2. Practice Python with Kaggle exercises
  3. Join r/learnmachinelearning

If you enjoy weeks 1-4, continue to the full roadmap below.

📋 Table of Contents

How to Use This Roadmap

  1. Follow the phases in order — each builds on the previous
  2. Complete the projects before moving on — they're not optional
  3. Mark your progress with checkboxes — fork this repo to track
  4. Estimated time: 12-18 months at 2-3 hours/day

💡 Pro tip: Fork this repo and check off items as you complete them. Your GitHub activity will show your learning journey.

Phase 0: Prerequisites (4-6 weeks)

Foundation before AI. Don't skip this.

Mathematics

Programming

📚 Phase 0 Books

Book Author Focus
Mathematics for Machine Learning Deisenroth et al. Math foundations (free PDF)
Think Stats Allen Downey Statistics with Python (free)
Python Data Science Handbook Jake VanderPlas NumPy, Pandas, Matplotlib (free)

🎯 Phase 0 Project

Build a data analysis project: Load a dataset, clean it, visualize insights, compute statistics.

Phase 1: Machine Learning Foundations (8-10 weeks)

Understanding the classics before deep learning.

Core Concepts

  • Supervised Learning - Regression, Classification
  • Unsupervised Learning - Clustering, Dimensionality Reduction
  • Model Evaluation - Train/test split, cross-validation, metrics

Algorithms to Master

Algorithm Type Learn It
Linear Regression Supervised Week 1-2
Logistic Regression Supervised Week 2-3
Decision Trees Supervised Week 3-4
Random Forests Ensemble Week 4-5
K-Means Unsupervised Week 5-6
PCA Dimensionality Week 6-7
SVM Supervised Week 7-8

🎓 Courses

Course Platform Duration Level
Machine Learning Specialization Coursera (Andrew Ng) 3 months Beginner
Google ML Crash Course Google 15 hours Beginner
StatQuest ML Playlist YouTube 20+ hours Beginner
CS229: Machine Learning Stanford Full semester Intermediate
Scikit-learn Tutorials Official Self-paced Beginner
Kaggle ML Course Kaggle 3 hours Beginner
Elements of AI University of Helsinki 30 hours Beginner

📚 Books

Book Author Level Notes
Hands-On ML with Scikit-Learn, Keras & TensorFlow Aurélien Géron Beginner Best practical intro
The Hundred-Page ML Book Andriy Burkov Beginner Free online, concise
An Introduction to Statistical Learning James et al. Intermediate Free PDF, classic
Pattern Recognition and ML Christopher Bishop Advanced Theoretical depth
Probabilistic ML Kevin Murphy Advanced Free, comprehensive

🎯 Phase 1 Projects

  1. Predict house prices - Regression with feature engineering
  2. Classify iris species - Multi-class classification
  3. Customer segmentation - K-means clustering
  4. Build ML pipeline - End-to-end with scikit-learn
  5. Kaggle competition - Join a beginner-friendly competition

Phase 2: Deep Learning (10-12 weeks)

Neural networks and the modern AI stack.

Fundamentals

  • Neural Network Basics - Perceptrons, activation functions, backpropagation
  • Optimization - SGD, Adam, learning rate scheduling
  • Regularization - Dropout, batch normalization, early stopping

Architectures

Architecture Use Case Weeks
MLP Tabular data 1-2
CNN Images 3-4
RNN/LSTM Sequences 5-6
Transformers Text, everything 7-10

🎓 Courses

Course Platform Duration Level
Fast.ai Practical Deep Learning Fast.ai 7 weeks Beginner
Deep Learning Specialization Coursera (Andrew Ng) 5 months Intermediate
CS231n: CNNs for Visual Recognition Stanford Full semester Intermediate
CS224n: NLP with Deep Learning Stanford Full semester Intermediate
MIT 6.S191: Intro to Deep Learning MIT 1 week intensive Beginner
NYU Deep Learning NYU (Yann LeCun) Full semester Intermediate
Neural Networks: Zero to Hero Andrej Karpathy 10+ hours Beginner
PyTorch Official Tutorials PyTorch Self-paced Beginner
TensorFlow Tutorials TensorFlow Self-paced Beginner
Hugging Face NLP Course Hugging Face Self-paced Intermediate

📚 Books

Book Author Level Notes
Deep Learning Goodfellow, Bengio, Courville Intermediate The bible (free online)
Dive into Deep Learning Zhang et al. Intermediate Interactive, free
Deep Learning with Python François Chollet Beginner Keras creator
Grokking Deep Learning Andrew Trask Beginner Build from scratch
Deep Learning from Scratch Seth Weidman Beginner Fundamentals
Neural Networks and Deep Learning Michael Nielsen Beginner Free online, intuitive
Understanding Deep Learning Simon Prince Intermediate Free PDF, 2023

🎯 Phase 2 Projects

  1. Image classifier - CNN on CIFAR-10 or custom dataset
  2. Sentiment analysis - RNN/LSTM on movie reviews
  3. Neural style transfer - Artistic image transformation
  4. Train a small transformer - From scratch understanding
  5. Fine-tune a pretrained model - Transfer learning

Phase 3: Specialization Tracks (12-16 weeks)

Choose your path. You can do multiple.

Track A: Computer Vision

  • Object Detection (YOLO, Faster R-CNN)
  • Image Segmentation (U-Net, Mask R-CNN)
  • Generative Models (GANs, Diffusion)
  • Video Understanding
  • 3D Vision

Courses:

Course Platform Level
CS231n: Deep Learning for Computer Vision Stanford Intermediate
CS231A: Computer Vision Stanford Advanced
First Principles of Computer Vision YouTube Intermediate
Roboflow Computer Vision Roboflow Beginner

Books:

  • "Deep Learning for Computer Vision" - Rajalingappaa Shanmugamani
  • "Computer Vision: Algorithms and Applications" - Richard Szeliski (free online)

Projects:

  • Real-time object detection system
  • Image generation with Stable Diffusion
  • Face recognition pipeline

Track B: Natural Language Processing

  • Word Embeddings (Word2Vec, GloVe)
  • Transformer Architecture (deep dive)
  • Large Language Models (GPT, BERT, LLaMA)
  • Prompt Engineering & Fine-tuning
  • RAG Systems

Courses:

Course Platform Level
CS224n: NLP with Deep Learning Stanford Intermediate
Hugging Face NLP Course Hugging Face Intermediate
CS685: Advanced NLP UMass Advanced
CMU Advanced NLP CMU (Graham Neubig) Advanced
Prompt Engineering Guide DAIR.AI Beginner

Books:

  • "Speech and Language Processing" - Jurafsky & Martin (free online)
  • "Natural Language Processing with Transformers" - Tunstall et al.

Projects:

  • Build a chatbot with fine-tuned model
  • Document Q&A system with RAG
  • Sentiment analysis API

Track C: Reinforcement Learning

  • MDPs and Bellman Equations
  • Q-Learning, DQN
  • Policy Gradients, A2C, PPO
  • Multi-Agent RL
  • Model-Based RL

Courses:

Course Platform Level
Spinning Up in Deep RL OpenAI Intermediate
David Silver's RL Course DeepMind Intermediate
CS285: Deep RL UC Berkeley Advanced
Hugging Face Deep RL Course Hugging Face Intermediate

Books:

  • "Reinforcement Learning: An Introduction" - Sutton & Barto (free online)
  • "Deep Reinforcement Learning Hands-On" - Maxim Lapan

Projects:

  • Train agent to play Atari games
  • Robot simulation with MuJoCo/Isaac Gym
  • Multi-agent environment

Track D: Generative AI & LLMs

  • Transformer architecture deep dive
  • Training LLMs (pretraining, SFT, RLHF)
  • Prompt engineering
  • Fine-tuning (LoRA, QLoRA)
  • RAG and agents

Courses:

Course Platform Level
Generative AI with LLMs Coursera (AWS/DeepLearning.AI) Intermediate
LLM Course GitHub Intermediate
Full Stack LLM Bootcamp FSDL Intermediate
Prompt Engineering for ChatGPT Vanderbilt Beginner

Projects:

  • Build a RAG application
  • Fine-tune LLaMA on custom data
  • Create an AI agent with tool use

Track E: AI Safety & Alignment

  • Alignment Problem Overview
  • RLHF (Reinforcement Learning from Human Feedback)
  • Interpretability & Explainability
  • Red-teaming and adversarial attacks
  • Constitutional AI

Resources:

Resource Type Level
AI Safety Fundamentals Course Beginner
Anthropic Research Papers Advanced
AI Alignment Forum Community All
MIRI Research Papers Advanced

Phase 4: Research Skills (8-12 weeks)

From practitioner to scientist.

Reading Papers

Reproducing Research

  • Pick 3 papers and reproduce results
  • Document discrepancies
  • Understand ablation studies

Experimentation

  • Hyperparameter tuning (Optuna, Ray Tune)
  • Experiment tracking (MLflow, Weights & Biases)
  • Statistical significance testing

Writing

  • Learn LaTeX (Overleaf)
  • Understand paper structure
  • Write a technical blog post

🎯 Phase 4 Projects

  1. Reproduce a paper - Pick SOTA from 2023-2024, reproduce it
  2. Improve on it - Add your own modification, measure impact
  3. Write it up - Blog post or arXiv preprint

Phase 5: Original Research (Ongoing)

Contributing new knowledge.

Finding Research Questions

  • Identify gaps in existing work
  • Start with "What if...?" questions
  • Build on recent papers

Research Workflow

Idea → Literature Review → Hypothesis → Experiment → Analysis → Write → Peer Review

Collaboration

  • Join research communities (Discord, Twitter/X)
  • Attend conferences (NeurIPS, ICML, ICLR, ACL)
  • Contribute to open source research

Publication Venues

Type Examples
Top Conferences NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP
Journals JMLR, TMLR, Nature Machine Intelligence
Preprints arXiv
Workshops Conference workshops (easier entry)

Additional Resources

YouTube Channels

Channel Focus
3Blue1Brown Math intuition
StatQuest Statistics & ML
Andrej Karpathy Deep learning from scratch
Yannic Kilcher Paper explanations
Two Minute Papers Research summaries
Sentdex Practical Python ML
Jeremy Howard Fast.ai
DeepMind Research talks
Lex Fridman AI interviews

Podcasts

Newsletters

Communities

Community Platform
r/MachineLearning Reddit
r/learnmachinelearning Reddit
ML Discord servers Discord
Hugging Face Forums Web
Kaggle Forums Web

Tools & Environment

Essential Setup

# Environment
python >= 3.10
conda or venv
jupyter notebook/lab
git

# Core libraries
numpy, pandas, matplotlib, seaborn
scikit-learn
pytorch or tensorflow

# Experiment tracking
wandb or mlflow

# Paper writing
LaTeX (Overleaf)

Compute Resources

Resource Type Cost
Google Colab GPU Free tier available
Kaggle Notebooks GPU Free (30h/week)
Lightning.ai GPU Free tier
Lambda Labs Cloud GPU Paid
Vast.ai Cloud GPU Cheap
RunPod Cloud GPU Cheap
Your own GPU Local RTX 3090+ recommended

Timeline Overview

Phase Duration Focus
0 4-6 weeks Math + Python
1 8-10 weeks Classical ML
2 10-12 weeks Deep Learning
3 12-16 weeks Specialization
4 8-12 weeks Research Skills
5 Ongoing Original Research

Total: 12-18 months to research-ready

Contributing

Found a great resource? PRs welcome!

License

CC0

Start today. The best time to begin was yesterday. The second best time is now.

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Complete roadmap from zero to AI Scientist - Free resources, projects, and milestones

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