Machine Learning Engineer Roadmap

From maths and Python to deploying models that real products depend on.

8 milestones · ~300 hours total

1. 1

   Maths you actually use

   ~40h

   Linear algebra, calculus intuition, probability and statistics, studied through code rather than only theory.

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2. 2

   Python and data tooling

   ~30h

   NumPy, pandas, Matplotlib and notebooks; reproducible environments and version control.

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3. 3

   Classical machine learning

   ~50h

   Regression, classification, trees, ensembles, clustering, cross-validation and avoiding leakage.

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4. 4

   Evaluation and error analysis

   ~20h

   Choose metrics that match the goal, read confusion matrices, and diagnose bias, variance and data issues.

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5. 5

   Deep learning basics

   ~50h

   Neural networks, backpropagation, CNNs and sequence models with PyTorch.

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6. 6

   MLOps

   ~30h

   Package a model behind an API, track experiments, monitor drift and retrain safely.

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7. 7

   Projects and portfolio

   ~50h

   Two end-to-end projects: data collection, modelling, a deployed demo and a written report of what failed.

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8. 8

   Interview preparation

   ~30h

   ML fundamentals, statistics, coding in Python and designing an ML system for a product.

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