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A practical, India-centric AI Engineer roadmap that takes learners from math & ML basics through production-grade model engineering and deployment. It emphasizes hands-on projects, MLOps, ethics, and domain specialization. Every paid course suggestion includes an explicit free alternative so learners never get blocked by paywalls.
Build the mathematical intuition (linear algebra, probability, statistics), Python skills, and core ML basics (supervised learning, model evaluation).
Linear algebra (vectors, matrices), calculus basics (gradients), probability and statistics (distributions, expectations, hypothesis testing).
Numpy, Pandas, Matplotlib/Seaborn, and basic scripting: data cleaning, EDA and reproducible notebooks.
Supervised vs unsupervised methods, evaluation metrics, cross-validation, bias-variance tradeoff and feature engineering.
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