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Welcome to the Machine Learning Road Map: Your guide to learning ML fundamentals for free!
This guide will equip you with:
- Essential ML foundations - Master the mathematical and programming fundamentals that underpin ML.
- Core ML concepts - Understand the key principles and algorithms that drive machine learning.
- Implementation fundamentals - Gain the conceptual knowledge needed to start building ML systems.
- Career preparation - Know the skills that employers value in ML professionals.
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*.
Before you begin:
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Let's go! 🚀
Table of Contents
General Programming
- 📚 CS50 (Intro to Programming and Computer Science) by Harvard
- 📚 Google's Python Class by Google
- 📘 NumPy Tutorial by NumPy Team
- 📘 Pandas Course by Kaggle
- 📐 Algebra Curriculum by Khan Academy
- 📐 Linear Algebra by Khan Academy
- 📊 Probability by Harvard
- 📈 Derivatives/Partial Derivatives by Khan Academy
- 📈 Gradients by Khan Academy
- 📈 Backpropagation Visualization by Google
- 🛠️ Learn Git by Git Community
- 🛠️ Github Tutorial by GitHub
- 🛠️ Learn Shell by learnshell.org
Core Machine Learning
- 📚 Spinning Up in RL by OpenAI
- 📚 NLP Course by Huggingface
- 📚 Computer Vision by Kaggle
- 📘 ML for Science by Christoph Molnar & Timo Freiesleben
- 🎮 ML for Games by Huggingface
- 📚 Intro to SQL and Advanced SQL by Kaggle
- 📚 Data Preparation by Google
- 🛠️ Made with ML by Goku Mohandas
- 🎓 ML School by Santiago
- 📐 ML Mathematics by Tivadar Danka
- 📈 ML Efficiency by MIT
- 📘 Knowledge Distillation by Dmitry Kozlov
- 📚 AI Ethics by Kaggle
- 📚 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
- 📘 System Design Interview by Alex Xu
- Study Plan for ML Interviews by Khang Pham
- 📚 Intro to Python by Harvard
- 📚 Python Deep Dive by Stephen Gruppetta
- 📚 C++ Tutorial by freeCodeCamp
- 📘 Rust by Rust Team
- 📚 Java by University of Helsinki
Deep Learning
- 📚 TensorFlow 2.0 Complete Course by freeCodeCamp
- 📚 PyTorch for Deep Learning by Daniel Bourke
- 📘 Scikit-learn Tutorials by Scikit-learn Developers
- 📘 Keras Tutorial by TutorialsPoint
Data Processing
- 📘 NumPy Tutorial by NumPy Team
- 📚 Pandas Course by Kaggle
Advanced Tools
- 🛠️ JAX Quickstart by Google
- 🛠️ ONNX Tutorial by ONNX Team
- 🛠️ TensorRT Guide by NVIDIA
- 🛠️ LangChain Crash Course by Patrick Loeber
Model Development
- 📘 XGBoost Documentation by XGBoost Team
- 📘 CUDA Programming Guide by NVIDIA
Major Providers
- 🛠️ ML on Google Cloud by Google Cloud
- 🛠️ AWS Machine Learning by Amazon Web Services
- 🛠️ Azure AI Fundamentals by Microsoft
Top Choices
- 🖥️ Google Colab
Free T4/P100 GPUs, limited time
- 🖥️ Kaggle Notebooks
30 hours/week of P100/T4 GPU
Additional Options
- 🖥️ Lightning AI
22 GPU hours free
- 🖥️ Google Cloud Platform
$300 free credits
- 🖥️ Amazon SageMaker
Free tier available
- 🖥️ Paperspace Gradient
Free community tier
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.
