Start with Python and math, progress to core machine learning, then deep learning and one specialization (NLP, CV, RecSys, or GenAI), while building deployable projects and basic MLOps skills; a disciplined 6–12 month plan is enough to become job‑ready for many entry‑level AI roles.
Stage 1: Foundations (Weeks 1–8)
- Math and stats: linear algebra (vectors, matrices), calculus for optimization, probability and statistics; practice by deriving loss/gradient for simple models and interpreting confidence/uncertainty.
- Python and data: Python basics, NumPy/pandas, plotting, and unit tests; clean and explore datasets with EDA and simple baselines.
- Mini‑projects: EDA notebook → baseline classifier/regressor with metrics (accuracy/F1/MAE), plus a concise model card summarizing data, method, and limits.
Stage 2: Core ML (Weeks 9–16)
- Algorithms and practice: linear/logistic regression, trees/ensembles, regularization, feature engineering, imbalanced data handling, calibration and cross‑validation.
- Reproducibility: fixed splits/seeds, experiment tracking, and error analysis; compare baseline → tuned model with cost‑sensitive metrics.
- Portfolio project: a tabular business problem (e.g., churn/fraud/demand) with a pipeline, validation, and a short business impact note.
Stage 3: Deep Learning (Weeks 17–28)
- Frameworks: PyTorch/TensorFlow, training loops, optimizers, schedulers, regularization; implement CNN/RNN/Transformer and compare.
- Specialization pick: NLP (tokenization, embeddings, sequence models, prompting) or CV (transfer learning, augmentation, detection/segmentation).
- Serving: deploy a model behind an API, add basic monitoring (latency, drift), and define rollback; include a README with setup and tests.
Stage 4: Specialize (Weeks 29–40)
- NLP/GenAI track: build a retrieval‑augmented generation (RAG) app with offline evaluations, guardrails, and cost/latency dashboards; document failure modes.
- CV track: fine‑tune a detector/segmenter with data versioning and per‑class precision‑recall; add a lightweight labeling loop.
- RecSys/time‑series: implicit feedback, sequence models, and business KPIs (CTR/retention); sketch an A/B testing plan.
Stage 5: MLOps and safety (ongoing)
- Pipelines and ops: containers, CI/CD, environment management, and feature stores; add data/label quality checks and canary deploys.
- Evaluation and ethics: bias/fairness checks, privacy by design, model cards, and incident playbooks; treat AI like production systems with SLOs.
90‑day execution plan
- Month 1: Python, math refresh, and one tabular ML project with a model card and baseline→tuned comparison; daily Python/SQL drills.
- Month 2: deep learning basics; deploy one NLP/CV model with a small API, logs, and rollback; record a 2‑minute demo.
- Month 3: specialization project (RAG/agent or CV detector) with offline evals and cost/latency metrics; add CI/CD and a short safety note; start applying with tailored resumes.
Projects that convert to interviews
- Tabular: churn or fraud model with calibration and a decision threshold tied to costs; include drift alerts and a simple dashboard.
- NLP/GenAI: RAG app over your notes or policies with eval sets, guardrails, and a failure taxonomy; show cost per query.
- CV: defect detection or medical‑like classification with per‑class metrics and data versioning; deploy a demo with feedback capture.
Certifications and resources (optional)
- Courses: Andrew Ng’s ML, fast.ai for practical DL, and interactive platforms for hands‑on practice.
- Career‑oriented guides and roadmaps: structured month‑by‑month learning with projects and curated resources.
Tips to stay consistent
- Timebox daily practice (45–90 minutes), keep a learning log, and convert each module into a small artifact (repo + README + demo).
- Use pre‑trained models and transfer learning to avoid heavy compute; rent GPUs briefly when needed.
- Join communities and share progress for feedback and referrals; iterate based on code reviews and issue reports.
Bottom line: follow a staged plan—Python and math → core ML → deep learning → one specialization—while shipping 2–3 deployable projects with clear metrics and safety notes; this combination reliably builds competence and credibility for AI roles in 2025.
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