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Welcome to the Machine Learning Road Map: A succinct guide to learning ML fundamentals for free!
This streamlined guide will help you:
- Learn essential prerequisites
- Master core ML concepts efficiently
- Build a foundation to understand advanced topics
- Get ready for real-world ML development
Unlike comprehensive guides that can be overwhelming, this road map is streamlined and focuses on the most important topics from the best ML educators. The goal is simple: to get you to a point where you can confidently explore ML topics independently.
Please support the authors and creators of these resources! Many of these resources had hundreds of hours put into them. If you purchase a book linked in the advanced topics section, don't forget to leave a review after reading it! Reviews are vital for authors to continue their work. I've linked to social profiles throughout the document as much as I could. You can support the creators of these resources for free by giving them a follow and liking their content.
Let's begin your ML journey! ๐
Table of Contents
General Programming
Python
- ๐ Intro to Python by Harvard
For beginners
- ๐ Google's Python Class by Google
Great as a refresher
Data Libraries
- ๐ NumPy Tutorial by NumPy Team
- ๐ Pandas Course by Kaggle
Foundation
- ๐ Algebra Curriculum by Khan Academy
- ๐ Linear Algebra by Khan Academy
Advanced Topics
- ๐ Probability by Harvard
- ๐ Derivatives/Partial Derivatives by Khan Academy
- ๐ Gradients by Khan Academy
- ๐ Backpropagation Visualization by Google
Version Control
- ๐ ๏ธ Learn Git by Git Community
- ๐ ๏ธ Github Tutorial by GitHub
Command Line
- ๐ ๏ธ Learn Shell by learnshell.org
Core Machine Learning
- ๐ 20 Min Introduction to Machine Learning by Google
Perfect starting point for ML concepts
- ๐ Machine Learning Crash Course by Google
Comprehensive foundation in ML fundamentals
- ๐ Machine Learning Q and AI by Sebastian Raschka
A deep dive into a wide variety of advanced ML concepts
- ๐ฅ Intro to LLMs by Andrej Karpathy
- ๐ฅ Building and Fine-tuning LLMs by Sebastian Raschka
- ๐ Build an LLM From Scratch by Sebastian Raschka
- ๐ LLM Course Sections by Maxime Labonne
- ๐ Deep Learning Fundamentals by LightningAI
- ๐ Engineer's Guide to Deep Learning by Hironobu Suzuki
- ๐ Transformers Course by Hugging Face
- ๐ Spinning Up in RL by OpenAI
- ๐ NLP Course by Huggingface
- ๐ Computer Vision by Kaggle
- ๐ ML for Science by Christoph Molnar & Timo Freiesleben
- ๐ฎ ML for Games by Huggingface
- ๐ Intro to SQL and Advanced SQL by Kaggle
- ๐ Data Preparation by Google
- ๐ ๏ธ Made with ML by Goku Mohandas
- ๐ ML School by Santiago
- ๐ ML Mathematics by Tivadar Danka
- ๏ฟฝ๏ฟฝ๏ฟฝ ML Efficiency by MIT
- ๐ Knowledge Distillation by Dmitry Kozlov
- ๐ AI Ethics by Kaggle
- ๐ ML Explainability by Kaggle
This sections contains popular skills on machine learning-related job listings and resources to prepare for interviews for those jobs.
- Cracking the Coding Interview by Gayle Laakman McDowell
Create for understanding and practicing Leetcode-style questions
- ๐ System Design Interview by Alex Xu
Preparation for system design
- Study Plan for ML Interviews by Khang Pham
A minimum viable study plan for machine learning interviews
- ๐ Intro to Python by Harvard
Comprehensive beginner-friendly Python course
- ๐ Python Deep Dive by Stephen Gruppetta
More advanced and comprehensive
- ๐ C++ Tutorial by freeCodeCamp
Complete C++ course for beginners
- ๐ Rust by Rust Team
- ๐ Java by University of Helsinki
Deep Learning
- ๐ TensorFlow 2.0 Complete Course by freeCodeCamp
- ๐ PyTorch for Deep Learning by Daniel Bourke
- ๐ Scikit-learn Tutorials by Scikit-learn Developers
- ๐ Keras Tutorial by TutorialsPoint
Data Processing
- ๐ NumPy Tutorial by NumPy Team
- ๐ Pandas Course by Kaggle
Advanced Tools
- ๐ ๏ธ JAX Quickstart by Google
- ๐ ๏ธ ONNX Tutorial by ONNX Team
- ๐ ๏ธ TensorRT Guide by NVIDIA
- ๐ ๏ธ LangChain Crash Course by Patrick Loeber
Model Development
- ๐ XGBoost Documentation by XGBoost Team
- ๐ CUDA Programming Guide by NVIDIA
Major Providers
- ๐ ๏ธ ML on Google Cloud by Google Cloud
- ๐ ๏ธ AWS Machine Learning by Amazon Web Services
- ๐ ๏ธ Azure AI Fundamentals by Microsoft
- ๐ ๏ธ Kubernetes Tutorial by TechWorld with Nana
- ๐ ๏ธ Docker Tutorial by freeCodeCamp
Top Choices
- ๐ฅ๏ธ Google Colab
Free T4/P100 GPUs, limited time
- ๐ฅ๏ธ Kaggle Notebooks
30 hours/week of P100/T4 GPU
Additional Options
- ๐ฅ๏ธ Lightning AI
22 GPU hours free
- ๐ฅ๏ธ Google Cloud Platform
$300 free credits
- ๐ฅ๏ธ Amazon SageMaker
Free tier available
- ๐ฅ๏ธ Paperspace Gradient
Free community tier
- ๐ฐ Recommended newsletters by me
- ๐ฅ Recommended YouTube channels by Dair AI
- ๐ฆ Recommended accounts to follow on X by me
If any information is missing, you are the author of a resource and you'd like it removed, or any other general feedback send me a message to let me know.