[Zero Two]

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.