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[Resources] Getting Started with Machine Learning - Zero Two - 06-21-2026
Prerequisites
Before jumping into ML you want solid Python, basic linear algebra (vectors, matrices, dot products), and some statistics (probability, distributions, mean/variance).
Foundational Courses
- fast.ai (fast.ai) - Top-down practical approach. Best first ML course if you learn by doing.
- Andrew Ng's Machine Learning Specialisation (Coursera) - Classic. Bottom-up theory, very clear explanations.
- CS231n (Stanford) - Computer vision focus, lecture notes and slides free online, excellent depth
- Andrej Karpathy's YouTube - "Neural Networks: Zero to Hero" series is the best free deep learning curriculum right now
Key Libraries
- PyTorch - Dominant in research, flexible, dynamic graphs. Start here.
- TensorFlow / Keras - Strong in production and mobile deployment
- scikit-learn - Traditional ML (trees, SVMs, clustering, preprocessing). Still the right tool for most tabular data problems.
- Hugging Face Transformers - Pre-trained model hub for NLP, vision, audio
- LangChain / LlamaIndex - LLM application frameworks
Free GPU Resources
- Google Colab - Free T4, A100 sometimes available
- Kaggle Notebooks - Free T4x2, good for competitions
- Lightning.ai - Generous free tier with A10G access
Papers
ArXiv (arxiv.org) hosts everything. For finding what to read: paperswithcode.com tracks SOTA results per benchmark.