Applied Machine Learning with Python Bootcamp Certificate
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Bootcamp Mastery Certificate B10
Primary spine: freeCodeCamp / Jovian Machine Learning with Python and Scikit-Learn. Move from regression and classification through trees, random forests, XGBoost and unsupervised learning into a complete real-world project and Flask deployment, with an added cross-validation bridge for stronger model-selection discipline.
Benchmark: freeCodeCamp publishes the Jovian-taught Machine Learning with Python and Scikit-Learn course as a public video, and StatQuest publishes its cross-validation lesson publicly. The Zuyini Bootcamp Mastery Certificate is an independent curated build-and-proof credential and is not a provider-issued certificate.
Core Video Sequence
01 — Complete applied ML with Python course
02 — Linear regression + gradient descent
03 — Logistic regression for classification
04 — Decision trees + random forests
05 — How to approach ML projects
06 — Gradient boosting with XGBoost
07 — Unsupervised learning with scikit-learn
08 — Real-world ML project from scratch
09 — Deploy an ML model with Flask
10 — Evaluation bridge: cross-validation
Build-Along Checkpoints
- Checkpoint 1: Load a real tabular dataset, define target/metric and create a leakage-safe train/validation/test strategy before modeling.
- Checkpoint 2: Build linear/logistic baselines with reproducible preprocessing; interpret coefficients, residuals or class errors as appropriate.
- Checkpoint 3: Train decision tree and random forest models; compare bias/variance, feature importance and overfitting behavior.
- Checkpoint 4: Train/tune an XGBoost or gradient-boosting model with cross-validation; record the search space, metric and winning rationale.
- Checkpoint 5: Perform structured error analysis by segment and edge case; add one unsupervised or representation analysis where useful.
- Checkpoint 6: Package preprocessing + model into a reproducible pipeline, deploy a Flask API/app and test prediction/input failure paths.
7-Day Independent Capstone
- Day 1: Frame the problem, metric, constraints and decision use; create a data dictionary and leakage-safe split strategy.
- Day 2: Clean/explore data and build a simple baseline; document missingness, outliers, imbalance and preprocessing decisions.
- Day 3: Train linear/logistic, tree/random-forest and boosting models using reproducible pipelines and cross-validation.
- Day 4: Tune the best candidates, compare generalization, calibration/thresholds where relevant and perform segment-level error analysis.
- Day 5: Add explainability and robustness checks; document fairness/data-limit concerns when they matter to the application.
- Day 6: Deploy the selected pipeline behind Flask with input validation, tests and a simple latency/reliability check.
- Day 7: Publish repo, notebook/report, model card, CV results, error analysis, deployment instructions, demo video and decision memo.
Applied ML - Analyze, Model, Validate & Deploy
Complete a defensible tabular machine-learning project from raw data to a deployed prediction service. The final work must show data cleaning, baselines, feature choices, multiple model families, cross-validation, error analysis, reproducibility, a simple deployment and an honest model card describing limitations.
Proof Rubric — What Must Be Shown
- DATA: Cleaning, leakage control + split rationale
- BASELINES: Simple models + interpretable reference
- MODELS: Trees, forests, boosting + comparisons
- VALIDATION: Cross-validation + holdout discipline
- ERRORS: Segment/edge-case analysis + fixes
- REPRODUCIBILITY: Pipeline, environment + deterministic runs
- DEPLOYMENT: Working Flask inference + input tests
- COMMUNICATION: Model card, limits + decision-ready memo