Learning Path
Learning Path: ML Engineer
A practical path: ML foundations → applied projects → production skills → interviewing.
Step 1: Foundations
- Linear algebra basics, probability, and core ML concepts.
- Implement small models and understand evaluation.
Step 2: Applied projects
- 2-3 projects that show end-to-end thinking.
- Feature engineering and iteration discipline.
Step 3: Production ML skills
- Data pipelines, model deployment basics, monitoring.
- Understand trade-offs (latency, cost, drift).
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