Machine Learning Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B02
Primary spine: DataTalksClub Machine Learning Zoomcamp. A practical ML engineering path from problem framing and classical models through evaluation, deployment, deep learning and scalable serving.
Benchmark: DataTalksClub Machine Learning Zoomcamp -- DataTalksClub certification is cohort-dependent and project/peer-review based; self-paced video study remains available outside an eligible cohort. This is a Zuyini Bootcamp Mastery Certificate built around free ML Zoomcamp video materials, not a DataTalksClub-issued credential.
Core Video Sequence
01 — Course orientation: what machine learning is and how projects flow
02 — Supervised learning: regression, classification & ranking
03 — Regression build: car-price prediction project
04 — Classification build: churn prediction + feature importance
05 — Evaluation: accuracy, confusion matrix, precision/recall, ROC AUC & more
06 — Deployment: model serialization, API serving, FastAPI/Docker workshop
07 — Trees, random forests, gradient boosting & XGBoost
08 — Neural networks & deep learning; current implementation add-ons
09 — Serverless inference and production-serving patterns
10 — Kubernetes / scalable model serving + full course playlist
Build-Along Checkpoints
- Checkpoint 1: Frame an ML problem; define target, leakage risks, train/validation/test strategy and a reproducible baseline.
- Checkpoint 2: Regression/classification pipeline with feature engineering, regularization, metric selection and error analysis.
- Checkpoint 3: Compare tree-based models, random forest and gradient boosting/XGBoost; tune without leaking validation information.
- Checkpoint 4: Package a prediction service behind an API; create an isolated environment and Docker image; test requests end to end.
- Checkpoint 5: Complete one deep-learning build and understand when neural networks are justified versus classical ML.
- Checkpoint 6: Demonstrate one scalable serving pattern such as serverless inference or Kubernetes-based service deployment.
7-Day ML Engineering Capstone
- Day 1: Choose a real problem and dataset; define business/technical objective, baseline and success metric.
- Day 2: Clean/split data; build baseline; document leakage, missing-data and reproducibility decisions.
- Day 3: Train at least three model families; tune and compare using appropriate validation and error analysis.
- Day 4: Select the final model; package preprocessing + inference consistently; persist the artifact.
- Day 5: Expose inference through FastAPI or equivalent; containerize; add request validation and failure handling.
- Day 6: Deploy locally/cloud/serverless/Kubernetes; capture latency, memory/CPU and representative prediction behavior.
- Day 7: Publish repository, model card, demo video, evaluation report and a short production-readiness decision memo.
Functional Model Service Capstone
Choose a real problem and dataset, train and compare at least three model families with correct validation and error analysis, select and package a final model behind a consistent preprocessing + inference pipeline, expose it through an API, containerize it, deploy locally/cloud/serverless/Kubernetes, and publish a repository, model card, evaluation report, demo video and production-readiness memo.
Proof Rubric — What Must Be Shown
- PROBLEM: clear target, users/use case and success metric
- DATA: split logic, preprocessing, leakage controls
- MODELS: baseline + multiple candidates + tuning
- EVALUATION: correct metrics, CV/holdout, error analysis
- SERVING: working API with consistent preprocessing
- PACKAGING: reproducible environment + container
- DEPLOYMENT: running endpoint and runtime evidence
- COMMUNICATION: model card, README, report + demo video