ML Foundations

The Bias-Variance Tradeoff

17 min

Quiz: Underfitting vs Overfitting in Practice

Train/Validation/Test — The Three-Split Discipline

4 min

Quiz: Why You Need All Three Sets

Cross-Validation — When Data is Scarce

6 min

Quiz: K-Fold, Stratified, Time-Based CV

SUBMISSION: Milestone Project 1 — Problem Formulation: Framing a Real Business Problem as an ML Task

Decision Trees and Random Forests

Supervised LearningFREE PREVIEW

Decision trees and random forests, gradient boosting (XGBoost/LightGBM/CatBoost), regularization (L1/L2/early stopping), feature engineering for ML, and handling class imbalance.

110 min12 lessons

Quiz: How Trees Decide Splits, and Why Forests Help

Gradient Boosting — XGBoost, LightGBM, CatBoost

16 min

Quiz: Boosting Intuition and Hyperparameters

Regularization — L1, L2, Early Stopping

20 min

Quiz: Picking the Right Regularizer

Feature Engineering and Categorical Encoding

Quiz: Encoding, Scaling, Interactions

Class Imbalance — SMOTE, Class Weights, Threshold Adjustment

18 min

Quiz: When and How to Handle Imbalance

SUBMISSION: Project 2 — End-to-End Churn Prediction with Cost-Sensitive Loss & ROC/PR Curves

SUBMISSION: Project 3 — Feature Engineering & Preprocessing Pipeline with Scikit-Learn Pipelines

Evaluation Metrics — AUC, F1, Precision/Recall, Brier

Evaluation, Leakage, and UnsupervisedFREE PREVIEW

Picking the right metric (AUC/F1/precision-recall/Brier), spotting and preventing data leakage, hyperparameter tuning strategies, and core unsupervised techniques (clustering, PCA, UMAP).

90 min9 lessons

16 min

Quiz: Choosing Metrics for the Decision

Data Leakage — The #1 ML Bug

Quiz: Spotting and Preventing Leakage

Hyperparameter Tuning — Grid, Random, Bayesian

20 min

Quiz: Tuning Strategy That Works

Clustering and Dimensionality Reduction

Quiz: k-Means, Hierarchical, PCA, UMAP

SUBMISSION: Project 4 — Data Leakage Forensic Audit on a Temporal Credit Default Dataset

SUBMISSION: Capstone 1 — Model Interpretability & Bias Audit with SHAP Values

Live ML CasesFREE PREVIEW

Six end-to-end walkthroughs: churn prediction pipeline, diagnosing data leakage, tuning XGBoost for fraud, picking metrics for business decisions, customer segmentation via clustering, and a light MLOps deployment.

145 min2 lessons

45 min

SUBMISSION: Capstone 2 — Baseline-to-Boosted Model Comparison & Validation Suite

45 min