AI & Machine Learning💻 Technical CourseLearnAspire Certified

Python ML Engineering for Java Developers: Ship a Fraud Detection Pipeline with scikit-learn, pandas, and FastAPI

“Your Java Brain Is an Asset — Ship Your First ML Pipeline”

Build, evaluate, serialize, and serve a production-grade RandomForest fraud classifier on imbalanced real-world data — without ever Googling DataFrame syntax again.

Intermediate12h6 modules48 slides18 exercises24 quiz Qs✓ Verified Mar 2026
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  • ✓6 modules of content
  • ✓48 concept slides
  • ✓18 practical exercises
  • ✓24 quiz questions
  • ✓Capstone project
  • ✓LearnAspire certificate

Learning Outcomes

What you'll learn

→You will be able to load Kestrel Finance's Postgres transaction log into a pandas 2.2 DataFrame via SQLAlchemy 2.0, perform loc/iloc slicing, vectorized feature derivation, and groupby-agg transformations on 500K rows without reverting to explicit Java-style for-loops — and explain in code review exactly why each idiom is faster.
→You will be able to design and fit a scikit-learn 1.4 Pipeline composed of a ColumnTransformer (StandardScaler for numerics, OneHotEncoder for categoricals), SMOTE from imbalanced-learn 0.12, and a RandomForestClassifier — with SMOTE correctly scoped inside the Pipeline to prevent data leakage across the train/test boundary.
→You will be able to diagnose and fix the three most common production Pipeline failures — wrong ColumnTransformer column scope, SMOTE applied before the train/test split, and stratification omitted from a k-fold split on a 0.3%-fraud dataset — by reading the exact sklearn and pandas tracebacks they produce.
→You will be able to tune a RandomForestClassifier using RandomizedSearchCV over a defined hyperparameter grid on a 500K-row dataset, justify the choice of RandomizedSearchCV over GridSearchCV at that row count, and interpret precision, recall, and F1 per class from a full sklearn classification_report in the context of fraud detection's asymmetric cost structure.
→You will be able to serialize a fitted scikit-learn Pipeline to a versioned .pkl file using joblib 1.4, load it inside a FastAPI 0.111 endpoint with a typed Pydantic request schema, and serve a live fraud prediction with sub-200ms latency — producing a GitHub-committable repo a hiring manager can clone and run with a single docker-compose up.

The day after you finish

The day after completing this course, Maya Chen — or you — can open a Jupyter notebook or a Python script, connect to a Postgres transaction database via SQLAlchemy, load 500K rows into pandas, build a scikit-learn Pipeline with ColumnTransformer and SMOTE, tune it with RandomizedSearchCV, print a stratified classification report showing per-class precision and recall, serialize the fitted Pipeline to fraud_pipeline_v1.pkl with joblib, and run a FastAPI server that accepts a JSON transaction payload and returns a fraud probability — then push the entire repo to GitHub before the morning standup.

Who this is for

  • Primary: Backend or full-stack Java developer with 3–6 years of Spring Boot and Kubernetes experience who has completed an ML MOOC but has never shipped production Python code
  • Secondary: Data engineer with a JVM background who needs to own model training and evaluation pipelines rather than just data movement
  • Tertiary: ML team lead or engineering manager evaluating whether a transitioning Java developer can own an end-to-end Python ML deliverable

Prerequisites

  • 3+ years of Java in production — OOP, generics, collections, and exception handling are assumed fluent
  • Familiarity with REST API design (Spring MVC or JAX-RS) — FastAPI endpoint structure is taught by contrast, not from scratch
  • Relational database fluency (SQL joins, ResultSets, or JPA) — pandas merge patterns are mapped to these directly
  • Completion of any ML MOOC covering gradient descent, regularization, and bias-variance tradeoff — hyperparameter concepts are not re-explained
  • Git and GitHub basics — every module produces a committed artifact; branching strategy is not taught
  • Python environment setup: Python 3.12 installed, pip or conda available — environment creation is covered in Module 1 but not debated

Curriculum

6 modules · full breakdown

📚 Part of: Python and AI for Java DevelopersCourse 2 of 3

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Capstone Project

Kestrel Finance Fraud Detection API: End-to-End Pipeline from Postgres to FastAPI

Maya has two days before the board demo. She must produce a working fraud classifier that ingests Kestrel Finance's synthetic 500K-row Postgres transaction log (0.3% fraud prevalence, 6 numeric features, 3 categorical features, 4% missing values), trains a tuned RandomForestClassifier inside a scikit-learn Pipeline with ColumnTransformer preprocessing and SMOTE oversampling applied correctly within cross-validation folds, evaluates it with 5-fold stratified cross-validation and a held-out test set, and exposes it as a FastAPI endpoint — all version-pinned, Dockerized, and pushed to a public GitHub repository with a CI badge.

What you'll deliver

A GitHub repository containing: (1) fraud_pipeline_v1.pkl — a joblib-serialized fitted Pipeline with preprocessing and classifier; (2) classification_report.txt — sklearn output showing per-class precision, recall, F1, and support on the held-out test set; (3) confusion_matrix.png — a seaborn heatmap of the test-set confusion matrix with fraud/legitimate labels; (4) a running FastAPI service (main.py + Dockerfile) accepting POST /predict with a typed Pydantic schema and returning fraud_probability and predicted_class; (5) feature_engineering_decisions.md — a one-page technical memo explaining the four feature engineering choices made, the RandomizedSearchCV parameter grid, and why each final hyperparameter value was selected over alternatives.

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