The Machine Learning Roadmap (github.com)

The Machine Learning Road Map is a free, curated guide that maps a clear learning path from programming and math prerequisites through core ML concepts, deep learning, deployment tools, and job-readiness. It’s designed to get learners to a point where they can confidently explore ML independently, balancing fundamentals (algebra, linear algebra, probability, derivatives, backpropagation) with practical implementation (Python, NumPy, Pandas) and career skills (coding interviews, system design, language proficiency). The guide emphasizes ethical and explainability resources alongside algorithmic and efficiency topics, making it useful both for newcomers and engineers pivoting into ML. Technically, the roadmap collects vetted courses and tutorials: CS50, Google’s Python Class, Khan Academy math modules, ML Mathematics (Tivadar Danka), MIT’s ML Efficiency, and practical deep-learning tracks (TensorFlow 2.0, PyTorch, scikit-learn, Keras). It also covers advanced tooling and deployment—JAX, ONNX, TensorRT, LangChain—and points to major cloud ML offerings and free compute (GCP $300 credits, AWS SageMaker free tier, Lightning AI 22 GPU hours, Paperspace community tier). For job preparation it lists interview staples (Cracking the Coding Interview, system design guides) and notes community-minded guidance to support original content creators. Overall, it’s a streamlined, pragmatic syllabus that stitches together math, coding, model-building and production-readiness for the ML community.