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Intermediate~14 weeks6 steps

Machine Learning Engineer

Train and ship models, not just notebooks: math foundations, classical ML, deep learning, transformers, and MLOps. Best if you're already comfortable with Python.

  1. Math foundations

    Linear algebra, calculus intuition, and probability — enough to read ML explanations.

  2. Classical machine learning

    Regression, classification, and evaluation with scikit-learn.

  3. Deep learning

    Neural networks hands-on with PyTorch and the fast.ai course.

  4. NLP & transformers

    Tokenization, attention, and fine-tuning with the Hugging Face ecosystem.

  5. ML in production (MLOps)

    Versioning, pipelines, deployment, and monitoring for models in the real world.

  6. Projects & portfolio

    Compete, build, and publish work that proves you can ship ML.