AI & Machine Learning💻 Technical CourseLearnAspire Certified

Build & Defend a Real ML Classifier: Logistic Regression and Decision Trees on Messy Financial Data

“Build your first real ML model. Evaluate it. Defend it.”

Take a raw, imbalanced loan CSV from pandas to a peer-reviewed model card — using scikit-learn 1.4, stratified cross-validation, and a precision-recall tradeoff your manager can act on.

Intermediate12h6 modules52 slides18 exercises24 quiz Qs
🔥 Launch Price — 63% off. Limited time.
₹2,999₹7,999

One-time · Lifetime access · Certificate included

Sign in to Enroll
7-day money-back guarantee
  • ✓6 modules of content
  • ✓52 concept slides
  • ✓18 practical exercises
  • ✓24 quiz questions
  • ✓Capstone project
  • ✓LearnAspire certificate

Learning Outcomes

What you'll learn

→You will be able to load the CapitalRoute loan CSV, identify target leakage by inspecting feature-target correlation and temporal ordering, and drop or engineer features so that no post-approval information contaminates training data
→You will be able to build a full scikit-learn 1.4 Pipeline — including SimpleImputer, StandardScaler, OneHotEncoder via ColumnTransformer, and a LogisticRegression estimator with class_weight='balanced' — and fit it on a stratified train/test split without a single line of manual column manipulation outside the Pipeline
→You will be able to run stratified 5-fold cross-validation using cross_validate(), read the full sklearn classification_report(), and explain why accuracy is the wrong metric for an 18% default rate dataset — with the confusion matrix numbers to prove it
→You will be able to diagnose overfitting on a DecisionTreeClassifier by plotting train vs. validation learning curves with matplotlib 3.8, then reduce it by tuning max_depth and min_samples_leaf using GridSearchCV with cv=StratifiedKFold(5)
→You will be able to choose between logistic regression and a tuned decision tree by comparing their precision-recall curves and F1 scores, then write a 5-sentence model recommendation — naming the winning model, its CV F1 score, the precision-recall tradeoff rationale, and the deployment risk — that a non-technical manager can read and act on

The day after you finish

The day after completing this course, you will be able to open a raw financial CSV at work, build a scikit-learn Pipeline with imputation, encoding, and either a LogisticRegression or DecisionTreeClassifier, evaluate it with stratified k-fold cross-validation, produce a full classification_report() and learning curve plot, and hand your manager a written model card that states which model to deploy and why — all without asking anyone for help.

Who this is for

  • Primary: Backend Python developer or data analyst with 2–3 years of experience who writes clean application code and SQL daily but has never shipped a trained model to production
  • Secondary: Junior ML engineer in their first 12 months who completed an introductory ML course but can't yet make defensible model selection decisions on messy real-world data
  • Tertiary: Data engineering or analytics lead who needs to evaluate model-building work done by their team and understand what 'good' ML evaluation evidence looks like

Prerequisites

  • Fluent in Python 3: functions, list comprehensions, imports, debugging stack traces, and reading third-party library documentation without hand-holding
  • Comfortable with pandas DataFrames and SQL: can filter, group, join, and inspect datasets; understands mean, variance, and correlation at the level of writing a data quality report
  • No prior ML experience required — but you must know what a for loop is without being told

Curriculum

6 modules · full breakdown

📊 Part of: Data & ML Engineering Path

Step 1 — ML Basics
→Step 2 — Data Pipelines
→Step 3 — RAG & Search
→Step 4 — AI Systems
Next in path: Step 2 — Data Pipelines →
🏆

Capstone Project

The CapitalRoute Model Card: A Peer-Reviewed, Production-Ready Classification Report

Maya submits her final deliverable to the CapitalRoute risk committee. Using the complete CapitalRoute loan dataset (4,200 rows, 18% default rate), learners produce a single Jupyter notebook containing: data loading and leakage audit, a full sklearn Pipeline for both a tuned LogisticRegression (C optimised via GridSearchCV) and a tuned DecisionTreeClassifier (max_depth and min_samples_leaf optimised via GridSearchCV with StratifiedKFold(5)), stratified 5-fold CV results for both models, a precision-recall curve comparison plot, a learning curve plot for the final chosen model, and a structured 5-section model card written in Markdown inside the notebook. The peer-review step uses a graded rubric: reviewers check for leakage-free features, correct stratified splitting, proper CV methodology, honest metric reporting, and a business-grounded recommendation.

What you'll deliver

A Jupyter notebook (loan_default_model_card.ipynb) that runs clean from top to bottom on Python 3.11 + scikit-learn 1.4.2 + pandas 2.1.4, containing: the full ML Pipeline, GridSearchCV results for both models, a matplotlib precision-recall comparison plot, a learning curve plot, a sklearn classification_report() for the chosen model on the held-out test set, and a Markdown model card section stating dataset, features, CV F1 scores, precision-recall tradeoff rationale, and a one-paragraph deployment recommendation with stated risk

Sign in to Enroll →