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I want to be an AI / ML Engineer

Python → ML → Deep Learning → LLMs. 6–9 months.

Timeline: 6–9 months

ML roles reward both math intuition and shipping. Build models AND deploy them.

PythonLinear Algebra & StatsPandas / NumPyScikit-learnDeep Learning (PyTorch)LLMs & RAG

Your milestones

1

Months 1–2: Python + Math

Foundations.

  • NumPy, Pandas
  • Linear algebra
  • Probability & stats
  • Calculus basics
2

Months 3–4: Classical ML

Scikit-learn end-to-end.

  • Regression & classification
  • Trees & RF, XGBoost
  • Clustering, PCA
  • Cross-validation
3

Months 5–6: Deep Learning

PyTorch.

  • Neural nets
  • CNNs, RNNs
  • Transfer learning
  • 1 vision + 1 NLP project
4

Months 7–9: LLMs + Deployment

Ship real AI.

  • Transformers
  • RAG with LangChain
  • FastAPI + Docker
  • Cloud deployment

Outcomes by the end

4+ ML projects
1 deployed LLM app
Kaggle competition entry
ML-ready portfolio

Roadmaps for this path

Machine Learning & AI

From Python basics to neural networks, NLP, and real-world ML project deployment.

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