End-to-end curriculum from learning paradigms to hands-on regression & classification pipelines, evaluation metric strategies, hyperparameter tuning, feature engineering, advanced ensembles (XGBoost/LightGBM/Stacking), model interpretability (SHAP/LIME), MLOps deployment, and technical interview preparation.
Complete all 11 milestones to unlock and download your verified certificate (11 remaining).
Understand the four fundamental paradigms: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
Regression & Classification theory, mathematical equations, algorithm taxonomy, and bias-variance trade-offs.
Build, scale, train, evaluate (MAE, RMSE, R²), and serialize a LinearRegression pipeline with Joblib.
Train Logistic Regression on diagnostic data, plot Confusion Matrices, compute ROC-AUC, and run inference.
Mathematical induction for MAE, RMSE, R², Precision, Recall, F1-Score, and ROC-AUC curve selection.
Master K-Fold CV, GridSearchCV, RandomizedSearchCV, Bayesian optimization, and leak-proof pipeline architectures.
ColumnTransformer pipelines, categorical encodings, missing value imputation, and leak-proof SMOTE resampling.
Bagging vs Boosting mathematical principles, XGBoost, LightGBM leaf-wise training, and out-of-fold Stacking meta-learners.
Permutation importance, Partial Dependence Plots, LIME local surrogates, and Game-Theoretic SHAP explanations.
Pipeline serialization with Joblib, FastAPI REST serving, Docker containerization, and data/concept drift monitoring.
Supervised learning taxonomy, algorithm derivations, metric selection matrices, overfitting remedies, and scenario case studies.