A curated collection of the best free machine learning courses, books, tutorials, and resources for learners at all levels.
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Khan Academy Linear Algebra - Link
- 📖 Comprehensive coverage of vectors, matrices, and transformations
- ⏱️ ~40 hours of content
- 🎯 Perfect for beginners
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3Blue1Brown Essence of Linear Algebra - Link
- 📖 Visual intuition for linear algebra concepts
- ⏱️ ~3 hours of animated explanations
- 🎯 Ideal for visual learners
- Think Stats (Free eBook) - Link
- 📖 Probability and statistics for programmers
- 💻 Python-based examples
- 🎯 Practical approach to statistics
- MIT OpenCourseWare Calculus - Link
- 📖 Complete single-variable calculus course
- 🎥 Video lectures + problem sets
- 🎯 University-level rigor
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Python for Everybody - Link
- 📖 Complete Python course by Dr. Chuck
- 🎥 Video lectures + assignments
- 🎯 Perfect for complete beginners
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Automate the Boring Stuff - Link
- 📖 Practical Python programming
- 💻 Real-world examples
- 🎯 Learn by doing
- R for Data Science - Link
- 📖 Complete guide to R for data analysis
- 💻 Tidyverse ecosystem
- 🎯 Data science focused
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Andrew Ng's Machine Learning Course - Link
- 📖 The gold standard ML course
- ⏱️ ~60 hours
- 🎯 Mathematical foundations + practical implementation
- 💰 Free audit available
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MIT Introduction to Machine Learning - Link
- 📖 Computational thinking approach
- 🎥 Complete video lectures
- 🎯 Theory + Python implementation
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Fast.ai Practical Deep Learning - Link
- 📖 Top-down approach to deep learning
- 💻 Jupyter notebooks included
- 🎯 Get building models quickly
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CS229 Stanford Machine Learning - Link
- 📖 Advanced mathematical treatment
- 📄 Comprehensive lecture notes
- 🎯 Graduate-level depth
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Deep Learning Specialization (Audit) - Link
- 📖 5-course specialization by Andrew Ng
- 💻 TensorFlow implementations
- 🎯 Comprehensive coverage
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MIT 6.S191 Introduction to Deep Learning - Link
- 📖 Modern deep learning techniques
- 🎥 Annual updated content
- 🎯 Cutting-edge research
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CS231n: Convolutional Neural Networks - Link
- 📖 Computer vision with deep learning
- 📄 Excellent lecture notes
- 🎯 CNN architectures and applications
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CS224n: Natural Language Processing - Link
- 📖 NLP with deep learning
- 🎥 Video lectures available
- 🎯 Transformers and modern NLP
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The Elements of Statistical Learning - Link
- 📖 Comprehensive statistical learning theory
- 🎯 Advanced mathematical treatment
- 📄 600+ pages of depth
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Pattern Recognition and Machine Learning - Link
- 📖 Bayesian approach to ML
- 🎯 Theoretical foundations
- 📄 Classic textbook
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An Introduction to Statistical Learning - Link
- 📖 More accessible than ESL
- 💻 R code examples
- 🎯 Perfect for beginners
- Dive into Deep Learning - Link
- 📖 Interactive deep learning book
- 💻 Multiple framework implementations
- 🎯 Theory meets practice
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Titanic Survival Prediction - Link
- 📊 Classic beginner dataset
- 🎯 Binary classification
- 💡 Learn data preprocessing
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House Price Prediction - Link
- 📊 Regression problem
- 🎯 Feature engineering practice
- 💡 Real estate domain
- Build Your Own Neural Network - Link
- 💻 Implement from scratch
- 🎯 Understand backpropagation
- 💡 Multiple language examples
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UCI ML Repository - Link
- 📊 500+ datasets
- 🎯 Various domains and sizes
- 💡 Well-documented
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Kaggle Datasets - Link
- 📊 50,000+ datasets
- 🎯 Community-driven
- 💡 Regular competitions
- Computer Vision: CIFAR-10, ImageNet, COCO
- NLP: Common Crawl, Wikipedia dumps, GLUE
- Time Series: Yahoo Finance, Weather data, IoT sensors
- Scikit-learn - General-purpose ML
- TensorFlow/Keras - Deep learning
- PyTorch - Research-focused DL
- Pandas - Data manipulation
- NumPy - Numerical computing
- Matplotlib/Seaborn - Visualization
- Google Colab - Free GPU/TPU access
- Kaggle Kernels - Free compute + datasets
- Azure Notebooks - Microsoft's offering
- AWS SageMaker - Limited free tier
- 3Blue1Brown - Mathematical intuition
- Two Minute Papers - Latest research summaries
- Sentdex - Python ML tutorials
- StatQuest - Statistics explained simply
- Jeremy Howard - Fast.ai content
- The TWIML AI Podcast - Industry interviews
- Data Skeptic - Critical thinking in data
- Linear Digressions - Accessible ML topics
- Practical AI - Applied AI discussions
- "Attention Is All You Need" - Transformer architecture
- "ImageNet Classification with Deep CNNs" - AlexNet
- "Generative Adversarial Networks" - GANs introduction
- "BERT: Pre-training of Deep Bidirectional Transformers" - BERT
- r/MachineLearning - Research discussions
- r/LearnMachineLearning - Beginner-friendly
- r/DataScience - Broader data science topics
- ML Twitter - Active researcher community
- Weights & Biases - MLOps community
- Papers We Love - Paper discussions
- Cross Validated - Statistics Q&A
- Kaggle Forums - Competition discussions
- Stack Overflow - Programming help
We love contributions! Here's how you can help:
- Fork this repository
- Create a new branch (
git checkout -b feature/amazing-resource) - Add your resource following our format
- Commit your changes (
git commit -m 'Add amazing ML resource') - Push to the branch (
git push origin feature/amazing-resource) - Open a Pull Request
- All the amazing educators who make their content freely available
- The open-source community for building incredible tools
⭐ Star this repository if you find it helpful! 🔄 Fork it to create your own version! 📢 Share it with others who might benefit!